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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Stavros Tsimpoukis Dimitrios Tyrovolas Sotiris Ioannidis Ian F. Akyildiz George K. Karagiannidis Christos Liaskos Institute of Computer Science Foundation for Research and Technology Hellas Greece Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Crete Chania Greece Truva Inc Alpharetta GA USA Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon Computer Science Engineering Department University of Ioannina Ioannina Greece

ISBN: (数字)9798350365955

ISBN: (纸本)9798350365962

Lately a new approach to Extended Reality (XR), denoted as XR-RF, has been proposed which is realized by combining Radio Frequency (RF) Imaging and programmable wireless environments (PWEs). RF Imaging is a technique that aims to detect geometric and material features of an object through RF waves. On the other hand, the PWE focuses on the the conversion of the wireless RF propagation in a controllable, by software, entity through the utilization of Reconfigurable Intelligent Surfaces (RISs), which can have a controllable interaction with impinging RF waves. In that sense, this dynamic synergy leverages the potential of RF Imaging to detect the structure of an object through RF wavefronts and the PWE’s ability to selectively replicate those RF wavefronts from one spatial location to wherever an XR-RF mobile user is presently located. Then the captured wavefront, through appropriate hardware, is mapped to the visual representation of the object through machine learning models. As a key aspect of the XR-RF’s system workflow is the wavefront copying mechanism, this work introduces a new PWE configuration algorithm for XR-RF. Moreover, it is shown that the waveform replication process inevitably yields imprecision in the replication process. After statistical analysis, based on simulation results, it is shown that this imprecision can be effectively modeled by the gamma distribution.

关键词： Radio frequency Wireless communication Wireless sensor networks Analytical models Extended reality Simulation Receiving antennas

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High Quality, Low Bandgap a-Si Films and Devices Produced Using Chemical Annealing

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MRS Online Proceedings Library 2021年第1期1245卷 1-6页

作者： Vikram Dalal Ashutosh Shyam Dan Congreve Max Noack Dept. of Electrical and Computer Engr. and Microelectronics Research Center Iowa State University Ames USA

We report on the growth and properties of novel amorphous Silicon (a-Si:H) p-i-n devices prepared using chemical annealing with argon gas. The i layer in the p-i-n devices was grown using a layer by layer approach, where the growth of a very thin a-Si:H layer (7-30 angstroms) grown using a silane:argon mixture was followed by chemical anneal by argon ions. Repeated cycling of such growth/anneal cycles was used to produce the desired total thickness of the i layer. The thickness of the a-Si layer, and duration of the anneal time, were varied systematically. Pressure and power of the plasma discharge were also systematically varied. It was found that a thin a-Si layer, <10 angstroms, and low pressures which led to relatively high ion flux on the surface, gave rise to a significantly smaller bandgap in the device, as indicated by a significant lateral shift in the quantum efficiency vs. photon energy curve to lower energies. The smallest Tauc gap observed was in the range of 1.62 eV. Corresponding to this smaller bandgap, the current in the solar cell increased, and the voltage decreased. The Urbach energies of the valence band tail were also measured in the device, using the quantum efficiency vs. energy curve, and found to be in the range of45 meV, indicating high quality devices. Too much ion bombardment led to an increase in Urbach energy, and an increase in defect density in the material. Raman spectra of the device i layer indicated an amorphous structure. When hydrogen was added to argon during the annealing cycle, some materials turned microcrystalline, as indicated by the Raman spectrum, and confirmed using x-ray diffraction.

关键词： amorphous Si photovoltaic

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An AI-enabled Bias-Free Respiratory Disease Diagnosis Model using Cough Audio: A Case Study for COVID-19

arXiv

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arXiv 2024年

作者： Saeed, Tabish Ijaz, Aneeqa Sadiq, Ismail Qureshi, Haneya N. Rizwan, Ali Imran, Ali AI4Networks Research Center Dept. of Electrical & Computer Engineering University of Oklahoma United States AI4lyf Pakistan James Watt School of Engineering University of Glasgow United Kingdom

Cough-based diagnosis for Respiratory Diseases (RDs) using Artificial Intelligence (AI) has attracted considerable attention, yet many existing studies overlook confounding variables in their predictive models. These variables can distort the relationship between cough recordings (input data) and RD status (output variable), leading to biased associations and unrealistic model performance. To address this gap, we propose the Bias-Free Network (RBF-Net), an end-to-end solution that effectively mitigates the impact of confounders in the training data distribution. RBF-Net ensures accurate and unbiased RD diagnosis features, emphasizing its relevance by incorporating a COVID-19 dataset in this study. This approach aims to enhance the reliability of AI-based RD diagnosis models by navigating the challenges posed by confounding variables. A hybrid of a Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks is proposed for the feature encoder module of RBF-Net. An additional bias predictor is incorporated in the classification scheme to formulate a conditional Generative Adversarial Network (c-GAN) which helps in decorrelating the impact of confounding variables from RD prediction. The merit of RBF-Net is demonstrated by comparing classification performance with State-of-The-Art (SoTA) Deep Learning (DL) model (CNN-LSTM) after training on different unbalanced COVID-19 data sets, created by using a large-scale proprietary cough data set. RBF-Net proved its robustness against extremely biased training scenarios by achieving test set accuracies of 84.1%, 84.6%, and 80.5% for the following confounding variables - gender, age, and smoking status, respectively. RBF-Net outperforms the CNN-LSTM model test set accuracies by 5.5%, 7.7%, and 8.2%, respectively. © 2024, CC BY-NC-SA.

关键词： COVID-19

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Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

Learning on Transformers is Provable Low-Rank and Sparse: A ...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hongkang Li Meng Wang Shuai Zhang Sijia Liu Pin-Yu Chen Dept. Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY USA Dept. Data Science New Jersey Institute of Technology Newark NJ USA Dept. Computer Science and Engineering Michigan State University East Lansing MI USA IBM Thomas J. Watson Research Center Yorktown Heights NY USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models. However, due to the technical challenges of the non-convex optimization caused by the complicated architecture of Transformers, the theoretical study of why these methods can be applied to learn Transformers is mostly elusive. To the best of our knowledge, this paper shows the first theoretical analysis of the property of low-rank and sparsity of one-layer Transformers by characterizing the trained model after convergence using stochastic gradient descent. By focusing on a data model based on label-relevant and label-irrelevant patterns, we quantify that the gradient updates of trainable parameters are low-rank, which de-pends on the number of label-relevant patterns. We also analyze how model pruning affects the generalization while improving computation efficiency and conclude that proper magnitude-based pruning has a slight effect on the testing performance. We implement numerical experiments to support our findings.

关键词： Training Analytical models Adaptation models Computational modeling Neurons Stochastic processes Signal processing algorithms

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Wavefront Sensing and Correction via Compressive Sensing and Advanced Photonic Devices

Wavefront Sensing and Correction via Compressive Sensing and...

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IEEE International Conference on Communications Workshops, ICC

作者： Haitham S. Khallaf Aydin Amini Antony Orth Oliver Pitts Rafael Kleiman Steve Hranilovic Dept. of Electrical & Computer Engineering McMaster University Hamilton ON Canada Nuclear Research Center Egyptian Atomic Energy Authority Inshas Egypt Dept. of Engineering Physics McMaster University Hamilton ON Canada National Research Council of Canada Ottawa ON Canada

Turbulence-induced wavefront distortion poses a sig-nificant obstacle in the implementation of large constellation co-herent modulation techniques in optical satellite-to-ground feeder links. The challenging atmospheric conditions of low earth orbit satellite systems, characterized by short coherence times and small spatial coherence widths, necessitate the development of fast and simple wavefront estimation and correction methods. This paper presents an innovative solution to this problem, by proposing a wavefront sensing and correction system that utilizes fast optical phase shifters, optical grating couplers, and compressive sensing techniques to mitigate the effects of turbulence-induced wavefront distortion. Numerical simulations demonstrate that the proposed approach effectively combats wavefront distortion using a small measurement ratio of less than 0.001, where the measurement ratio is the ratio of the number of measurements to the total number of wavefront pixels. In addition, the proposed approach can effectively address phase mismatches between the optical carrier and the local oscillator.

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An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks

arXiv

引用

arXiv 2023年

作者： Ijaz, Aneeqa Raza, Waseem Farooq, Hasan Manalastas, Marvin Imran, Ali AI4Networks Research Center Dept. of Electrical & Computer Engineering University of Oklahoma United States James Watt School of Engineering University of Glasgow United Kingdom

Deep automation provided by self-organizing network (SON) features and their emerging variants such as zero touch automation solutions is a key enabler for increasingly dense wireless networks and pervasive Internet of Things (IoT). To realize their objectives, most automation functionalities rely on the Minimization of Drive Test (MDT) reports. The MDT reports are used to generate inferences about network state and performance, thus dynamically change network parameters accordingly. However, the collection of MDT reports from commodity user devices, particularly low cost IoT devices, make them a vulnerable entry point to launch an adversarial attack on emerging deeply automated wireless networks. This adds a new dimension to the security threats in the IoT and cellular networks. Existing literature on IoT, SON, or zero touch automation does not address this important problem. In this paper, we investigate an impactful, first of its kind adversarial attack that can be launched by exploiting the malicious MDT reports from the compromised user equipment (UE). We highlight the detrimental repercussions of this attack on the performance of common network automation functions. We also propose a novel Malicious MDT Reports Identification framework (MRIF) as a countermeasure to detect and eliminate the malicious MDT reports using Machine Learning and verify it through a use-case. Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks. © 2023, CC BY-NC-ND.

关键词： Automation

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A Thyristor-Based Fault Current Bypass Solid-State Circuit Breaker for DC Microgrid Applications 14

A Thyristor-Based Fault Current Bypass Solid-State Circuit B...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Zhou, Jiale Sun, Xiuhu Mu, Qiang Zhao, Tiefu University of North Carolina at Charlotte Dept. of Electrical & Computer Engineering CharlotteNC United States University of North Carolina at Charlotte Batt Cave Research Center Ece Department CharlotteNC United States

ISBN: (纸本)9798331516116

Thyristor-based solid-state circuit breaker (SSCB) has high efficiency as a protection device for DC microgrid distribution. However, the passive turn-off and microsecond-level turn-off time make thyristor-based SSCBs more suitable for high-inertia DC systems. Large inductance in DC systems extends the fault clearing time. This paper proposes a thyristor-based fault current bypass SSCB (TFCB-SSCB) for DC microgrids. The proposed topology, compared to conventional thyristor-based SSCBs, not only offers high efficiency but also achieves rapid isolation of the source and fault side, with the only additional cost being a switch with a low breakdown voltage. Initially, the superiority of the proposed topology is analyzed in comparison to the prior-art thyristor-based topology. Next, the operating principles and key design considerations are presented for the proposed topology. Finally, the effectiveness of the proposed topology is verified through simulation results on an 800 V/ 165 A TFCB-SSCB model and experimental results on a 100 V/68 A TFCB-SSCB prototype. The fault isolation time of the proposed topology is five times faster than that of the traditional topology in simulations and twice as fast in experiments. © 2025 IEEE.

关键词： Electric fault location

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Energy-Aware Trajectory Design for UAV-mounted Full-duplex Relays

Energy-Aware Trajectory Design for UAV-mounted Full-duplex R...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Dimitrios Tyrovolas Nikos Mitsiou Thomas Boufikos Sotiris Tegos Prodromos-Vasileios Mekikis Panagiotis Diamantoulakis Sotiris Ioannidis Christos Liaskos George Karagiannidis Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Dept. of Electrical and Computer Engineering Technical University of Crete Chania Greece Hilti Corporation Feldkircher Strasse 100 Schaan Liechtenstein Computer Science Engineering Department University of Ioannina Ioannina Greece Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

Unmanned aerial vehicles (UAVs) equipped with full-duplex relays (FDRs) are pivotal in overcoming connectivity challenges by dynamically establishing effective communication channels. However, despite their potential in network performance via trajectory optimization, integrating energy consumption models for UAV-mounted FDRs remains unexplored, crucial for trajectory design adhering to existing energy constraints. To this end, we introduce an energy-aware trajectory optimization framework to maximize network performance and user fairness within the UAV’s energy constraints. Specifically, we present a detailed energy consumption model describing the operational needs of UAV-mounted FDRs and formulate a joint time-division multiple access (TDMA) user scheduling-UAV trajectory optimization problem considering the power dynamics of UAV-mounted FDRs. Finally, our simulation results highlight the role of energy awareness in achieving optimal trajectory and scheduling, contributing to UAV-mounted FDRs’ performance in future networks.

关键词： Energy consumption Time division multiple access Trajectory planning Simulation Full-duplex system Network architecture Autonomous aerial vehicles Vehicle dynamics Relays Trajectory optimization

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SLIPT in Joint Dimming Multi-LED OWC Systems with Rate Splitting Multiple Access

SLIPT in Joint Dimming Multi-LED OWC Systems with Rate Split...

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IEEE International Conference on Communications (ICC)

作者： Sepideh Javadi Sajad Faramarzi Farshad Zeinali Hosein Zarini Mohammad Robat Mili Panagiotis D. Diamantoulakis Eduard Jorswieck George K. Karagiannidis Pasargad Institute for Advanced Innovative Solutions (PIAIS) Tehran Iran Dept. of Electrical Engineering Iran University of Science and Technology Tehran Iran Dept. of Computer Engineering Sharif University of Technology Tehran Iran Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Institute for Communications Technology Technische Universitat Braunschweig Braunschweig Germany Artificial Intelligence & Cyber Systems Research Center Lebanese American University (LAU) Lebanon

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Optical wireless communication (OWC) systems with multiple light-emitting diodes (LEDs) have recently been explored to support energy-limited devices via simultaneous lightwave information and power transfer (SLIPT). The energy consumption, however, becomes considerable by increasing the number of incorporated LEDs. This paper proposes a joint dimming (JD) scheme that lowers the consumed power of a SLIPT-enabled OWC system by controlling the number of active LEDs. We further enhance the data rate of this system by utilizing rate splitting multiple access (RSMA). More specifically, we formulate a data rate maximization problem to optimize the beamforming design, LED selection and RSMA rate adaptation that guarantees the power budget of the OWC transmitter, as well as the quality-of-service (QoS) and an energy harvesting level for users. We propose a dynamic resource allocation solution based on proximal policy optimization (PPO) reinforcement learning. In simulations, the optimal dimming level is determined to initiate a trade-off between the data rate and power consumption. It is also verified that RSMA significantly improves the data rate.

关键词： Wireless communication Energy consumption Quality of service Reinforcement learning Light emitting diodes Dynamic scheduling Real-time systems

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Iterative Power Control for Wireless Networks with Distributed Reconfigurable Intelligent Surfaces

arXiv

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arXiv 2022年

作者： Mao, Jiayu Yener, Aylin INSPIRE@OhioState Research Center Dept. of Electrical and Computer Engineering The Ohio State University United States

Reconfigurable Intelligent Surfaces (RIS) are a new paradigm which, with judicious deployment and alignment, can enable more favorable propagation environments and better wireless network design. As such, they can offer a number of potential benefits for next generation wireless systems including improved coverage, better interference management and even security. In this paper, we consider an uplink next generation wireless system where each user is assisted with an RIS. We study the uplink power control problem in this distributed RIS-assisted wireless network. Specifically, we aim to minimize total uplink transmit power of all the users subject to each user’s reliable communication requirements at the base station by a joint design of power, receiver filter and RIS phase matrices. We propose an iterative power control algorithm, combined with a successive convex approximation technique to solve the problem with non-convex phase constraints. Numerical results illustrate that distributed RIS assistance leads to uplink power savings when direct links are weak. Copyright © 2022, The Authors. All rights reserved.

关键词： Wireless networks

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