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2024 IEEE International Conference on Big Data, BigData 2024

作者： Liu, Hanwen Hajialigol, Daniel Antony, Benny Han, Aiguo Wang, Xuan Virginia Tech Department of Computer Science BlacksburgVA United States Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA United States Virginia Tech Department of Biomedical Engineering and Mechanics BlacksburgVA United States

ISBN: (纸本)9798350362480

Deciphering the intricacies of the human brain has captivated curiosity for centuries. Recent strides in Brain-computer Interface (BCI) technology, particularly using motor imagery, have restored motor functions such as reaching, grasping, and walking in paralyzed individuals. However, unraveling natural language from brain signals remains a formidable challenge. Electroencephalography (EEG) is a non-invasive technique used to record electrical activity in the brain by placing electrodes on the scalp. Previous studies of EEG-to-text decoding have achieved high accuracy on small closed vocabularies, but still fall short of high accuracy when dealing with large open vocabularies. We propose a novel method, EEG2Text, to improve the accuracy of open vocabulary EEG-to-text decoding. Specifically, EEG2Text leverages EEG pre-training to enhance the learning of semantics from EEG signals and proposes a multi-view transformer to model the EEG signal processing by different t spatial regions of the brain. Experiments show that EEG2Text has superior performance, outperforming the state-of-the-art baseline methods by a large margin of up to 5% in absolute BLEU and ROUGE scores. EEG2Text shows great potential for a high-performance open-vocabulary brain-to-text system to facilitate communication. Our code is available at: https://***/ForeverNightmare/EEG2Text. © 2024 IEEE.

关键词： Electroencephalography

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Wi-Six: Precise Positioning in the Metaverse via Optimal Wi-Fi Router Deployment in 6G Networks

Wi-Six: Precise Positioning in the Metaverse via Optimal Wi-...

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2023 IEEE International Conference on Metaverse Computing, Networking and Applications, MetaCom 2023

作者： Famili, Alireza Atalay, Tolga Stavrou, Angelos Wang, Haining Virginia Tech Department of Electrical and Computer Engineering VA United States

ISBN: (纸本)9798350333336

Mobile networks are evolving at a rapid pace to accommodate the growing versatility of use cases. 5G deployments have been designed in a way to offer services in the form of end-to-end logically isolated fragments called network slices. An increasingly popular service type in this delivery model is the augmented and virtual reality (AR/VR) wearables which will play a key role in the delivery of the Metaverse. In addition to rigorous latency control, accurate Metaverse realization depends highly on high-precision localization of such user equipment (UE) in a three-dimensional environment. Being a successor to 5G, 6G networks will operate at higher frequency ranges in denser deployments. In such environments, maintaining high-accuracy localization for indoor deployments will become a challenging task considering the signal characteristics at higher frequencies. To that end, we propose the Wi-Six framework, which relies on well-defined (third-generation partnership project) 3GPP principles to model a hybrid radio access network (RAN) composed of Wi-Fi routers that offload positioning from base stations (BSs) for indoor environments. Our contribution in this paper is to demonstrate that the placement geometry of Wi-Six transmission and reception points (TRPs) plays a significant role in the positioning accuracy of AR/VR Metaverse wearable equipment. We formulate and solve an NP-Hard problem of TRP placement using a novel optimization framework. We show that by using our optimal placement solution, positioning accuracy improves significantly compared to arbitrary anchor placements. © 2023 IEEE.

关键词： Optimization

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Integrated Global-Local Gaussian Attention for Image Compression

Integrated Global-Local Gaussian Attention for Image Compres...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Khoshkhahtinat, Atefeh Mehta, Piyush M. Department of Computer Science and Electrical Engineering West Virginia University WV United States

ISBN: (纸本)9798350368741

Transformers have demonstrated outstanding performance in learned image compression (LIC) due to their high capacity for modeling complex dependencies. However, existing methods employ window-based attention mechanisms to reduce overhead, which limits the long-range dependency modeling capabilities of transformers. In this study, we present a novel transformer block designed to efficiently leverage global spatial information while keeping computational costs low. Additionally, our proposed transformer incorporates a local attention module that utilizes Gaussian positional encoding to enhance spatial awareness. These improvements contribute to achieving less redundant latent representations and improving compression efficiency. In addition to enhance the decorrelating capability of the transformation component, we propose a novel channel-wise entropy model that effectively leverages channel dependencies, thereby providing a more accurate estimation of the latent space distribution. Experimental results indicate that our framework surpasses both conventional and neural codecs. © 2025 IEEE.

关键词： Entropy model Guassian positional encoding Learned Image Compression Transformer

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An Optimized Move List for Dynamic Protection Area in CBRS

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IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1382-1396页

作者： Jai, Naru Shi, Yi Olfat, Masoud Reed, Jeffrey H. DaSilva, Luiz Lou, Wenjing Hou, Y. Thomas Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Commonwealth Cyber Initiative Virginia Tech ArlingtonVA22203 United States Federated Wireless ArlingtonVA22203 United States Department of Computer Science Virginia Tech ArlingtonVA22181 United States

In Citizen Broadband Radio Service (CBRS), upon the activation of a Dynamic Protection Area (DPA), a subset of CBRS Devices (CBSDs) from both the Priority Access License (PAL) and General Authorized Access (GAA) users must be moved to other channels or cease transmission so that the 95th percentile aggregate interference level to the incumbent Navy radars, across all directional beams, can be kept under a pre-defined interference threshold. An important performance objective is to make this CBSD move list as small as possible so that the adverse impact on the PAL and GAA users can be minimized. Move lists obtained by the state-of-the-art algorithms tend to be unnecessarily large due to their designs;nor can they offer the required differentiated service between the PAL and GAA users. This paper presents an optimization-based algorithm (called OML) to find the minimum move list. The essence of OML encompasses holistic considerations of all DPA points and all beam directions simultaneously in its formulation, a two-stage optimization process with a priority to PAL CBSDs, and the use of a small subset of DPA points to expedite computation time. Experimental results show significant improvement of OML over the state-of-the-art in its abilities to find a smaller move list and to offer priority to PAL over GAA CBSDs while meeting interference protection at all DPA points. © 2015 IEEE.

关键词： Gallium arsenide

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Flexible multimaterial fibers in modern biomedical applications

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National Science Review 2024年第10期11卷 110-127页

作者： Jongwoon Kim Xiaoting Jia The Bradley Department of Electrical and Computer Engineering Virginia Tech School of Neuroscience Virginia Tech Department of Materials Science and Engineering Virginia Tech

Biomedical devices are indispensable in modern healthcare, significantly enhancing patients' quality of ***, there has been a drastic increase in innovations for the fabrication of biomedical devices. Amongst these fabrication methods, the thermal drawing process has emerged as a versatile and scalable process for the development of advanced biomedical devices. By thermally drawing a macroscopic preform, which is meticulously designed and integrated with functional materials, hundreds of meters of multifunctional fibers are produced. These scalable flexible multifunctional fibers are embedded with functionalities such as electrochemical sensing, drug delivery, light delivery, temperature sensing, chemical sensing, pressure sensing, etc. In this review, we summarize the fabrication method of thermally drawn multifunctional fibers and highlight recent developments in thermally drawn fibers for modern biomedical application, including neural interfacing, chemical sensing, tissue engineering, cancer treatment, soft robotics and smart ***, we discuss the existing challenges and future directions of this rapidly growing field.

关键词： biomedical fiber thermal drawing functional fiber biomedical devices

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Laser Machined Fiber‑Based Microprobe: Application in Microscale Electroporation

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Advanced Fiber Materials 2022年第4期4卷 859-872页

作者： Jongwoon Kim Yajun Zhao Shuo Yang Ziang Feng Anbo Wang Rafael V.Davalos Xiaoting Jia Bradley Department of Electrical and Computer Engineering Virginia TechBlacksburgVA 24060USA Department of Biomedical Engineering and Mechanics Virginia TechBlacksburgVA 24061USA

Microscale electroporation devices are mostly restricted to in vitro experiments(i.e.,microchannel and microcapillary).Novel fiber-based microprobes enable in vivo microscale electroporation and arbitrarily select the cell groups of interest to *** developed a flexible,fiber-based microscale electroporation device through a thermal drawing process and femtosecond laser micromachining *** fiber consists of four copper electrodes(80μm),one microfluidic channel(30μm),and has an overall diameter of 400μ*** dimensions of the exposed electrodes and channel were customizable through a delicate femtosecond laser *** feasibility of the fiber probe was validated through numerical simulations and in vitro *** reversible and irreversible microscale electroporation was observed in a 3D collagen scaffold(seeded with U251 human glioma cells)using fluorescent *** ablation regions were estimated by performing the covariance error ellipse method and compared with the numerical *** computational and experimental results of the working fiber-based microprobe suggest the feasibility of in vivo microscale electroporation in space-sensitive areas,such as the deep brain.

关键词： Fiber probe Polymer Micromachine Irreversible electroporation Reversible electroporation Microscale electroporation

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Multi-Input Deep Learning Models for Weight Forecasting of Pigs Using Depth Images 36

Multi-Input Deep Learning Models for Weight Forecasting of P...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Ranjan, Pranjal Ha, Dong Sam Morota, Gota Shin, Sook Bradley Department of Electrical and Computer Engineering Virginia Tech Virginia24061 United States School of Animal Sciences Virginia Tech Virginia24061 United States

ISBN: (纸本)9798331527235

Accurate weight forecasting is essential for optimizing swine farming operations and enhancing animal welfare. This paper introduces a novel approach for pig weight forecasting, employing multi-input deep learning models that harness both depth images and statistical descriptors. The study conducts a comprehensive comparison of traditional machine learning (ML) models, deep learning (DL) models, hybrid ML and DL models, and multi-input models integrating both time-series data and image features. A meticulously curated dataset comprising time-series weight measurements and corresponding depth images of pigs forms the foundation of the study. Image descriptors such as length, width, depth, and volume were extracted from the depth images. The proposed multi-input models, employing architectures based on ResNet, XCeption, LSTM, and GRU layers, are meticulously trained and evaluated using this dataset. The performance evaluation is conducted using mean absolute error (MAE) and mean absolute percentage error (MAPE) metrics. The results underscore the superiority of the multi-input models over traditional ML, DL, and hybrid models. Notably, the best-performing model achieves a test MAE of 1.81 kg and a test MAPE of 5.56 %. This exceptional performance highlights the importance of leveraging both time-series data and image features for precise weight forecasting in pigs. These findings can hold significant implications for improving the efficiency and sustainability of swine farming practices, offering a pathway towards improved decision-making and animal management protocols. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-Agent Decision Transformer for Power Control in Wireless Networks

Multi-Agent Decision Transformer for Power Control in Wirele...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yiming Yang, Kun Shen, Cong Guo, Dongning Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22904 United States

ISBN: (纸本)9798350368741

This paper introduces a novel offline approach to power control in wireless networks using a multi-agent reinforcement learning (MARL) framework. We develop a multi-agent decision transformer method to optimize performance metrics including sum-rate or packet delay. In this distributed method, each agent controls an individual link and determines its power level based on its own measurements and information exchange with a few agents within a limited neighborhood. Numerical results demonstrate that the proposed method achieves quality of service performance comparable to centralized methods using global information, for both sum-rate maximization and traffic-driven packet delay minimization problems. As an offline learning solution, it can efficiently leverage knowledge from existing mature techniques and offers significant advantages in the safety, stability, and convergence rate over existing online methods. This work provides a promising alternative for learning-based resource management in wireless networks. © 2025 IEEE.

关键词： decision transformer multi-agent reinforcement learning offline reinforcement learning (RL) radio resource management wireless networks

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Demystifying the Impact of Multi-RAT Ranging for 6G Metaverse Users 15

Demystifying the Impact of Multi-RAT Ranging for 6G Metavers...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Famili, Alireza Atalay, Tolga Stavrou, Angelos WayWave Inc VA United States Virginia Tech Department of Electrical & Computer Engineering VA United States

ISBN: (纸本)9798331507695

The next generation of mobile networks has been designed in a way to accommodate a wide variety of use cases with diverse Quality of Service (QoS) demands. Within the cellular networks of the future, services will be provided in isolated fragments referred to as network slices. Within this ecosystem, an up-and-coming use case is the advent of Augmented and Virtual Reality (AR/VR), which is crucial for fulfilling the vision of the Metaverse. To create a believable Metaverse, it's crucial to precisely locate user devices in addition to maintaining stringent latency controls. As we transition from 5G to 6G networks, it is expected that these networks will function at higher frequencies and in more compact environments. This shift poses a challenge for indoor ranging because signals at these frequencies behave differently. In pursuit of this objective, our work seeks to demystify the ranging performance of Metaverse Users (MUs) in heterogeneous cellular environments. Our evaluation approach leverages the established protocols of the Third Generation Partnership Project (3GPP), utilizing an integrated network of conventional cellular base stations and Wireless Fidelity (Wi-Fi) routers to facilitate both outdoor and indoor communications. Ultimately the goal of this paper is to show that the proposed hybrid approach effectively enhances indoor ranging by offloading the distance estimation responsibilities from outdoor base stations to indoor access points. © 2025 IEEE.

关键词： Base stations

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Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications 63

Hypergame Theory for Decentralized Resource Allocation in Mu...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Thomas, Christo Kurisummoottil Saad, Walid Virginia Tech Wireless@VT Bradley Department of Electrical and Computer Engineering ArlingtonVA United States

ISBN: (纸本)9798350316339

Semantic communications (SC) is an emerging communication paradigm in which wireless devices can send only relevant information from a source of data while relying on computing resources to regenerate missing data points. However, the design of a multi-user SC system becomes more challenging because of the computing and communication overhead required for coordination. Existing solutions for learning the semantic language and performing resource allocation often fail to capture the computing and communication tradeoffs involved in multiuser SC. To address this gap, a novel framework for decentralized computing and communication resource allocation in multi-user SC systems is proposed. The challenge of efficiently allocating communication and computing resources (for reasoning) in a decentralized manner to maximize the quality of task experience for the end users is addressed through the application of Stackelberg hypergame theory. Leveraging the concept of second-level hypergames, novel analytical formulations are developed to model misperceptions of the users about each other's communication and control strategies. Further, equilibrium analysis of the learned resource allocation protocols examines the convergence of the computing and communication strategies to a local Stackelberg equilibria, considering misperceptions. Simulation results show that the proposed Stackelberg hypergame results in efficient usage of communication and computing resources while maintaining a high quality of experience for the users compared to state-of-the-art that does not account for the misperceptions. © 2024 IEEE.

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