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Full-Duplex Wireless for 6G: Progress Brings New Opportunities and Challenges

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arXiv 2023年

作者： Smida, Besma Sabharwal, Ashutosh Fodor, Gabor Alexandropoulos, George C. Suraweera, Himal A. Chae, Chan-Byoung The Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL77005 United States The Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Ericsson Research KTH Royal Institute of Technology Stockholm Sweden The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Department of Electrical and Electronic Engineering University of Peradeniya Peradeniya20400 Sri Lanka The School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile networks. In less than ten years, in-band FD has advanced from being demonstrated in research labs to being implemented in standards, presenting new opportunities to utilize its foundational concepts. Some of the most significant opportunities include using FD to enable wireless networks to sense the physical environment, integrate sensing and communication applications, develop integrated access and backhaul solutions, and work with smart signal propagation environments powered by reconfigurable intelligent surfaces. However, these new opportunities also come with new challenges for large-scale commercial deployment of FD technology, such as managing self-interference, combating cross-link interference in multi-cell networks, and coexistence of dynamic time division duplex, subband FD and FD networks. © 2023, CC BY-NC-ND.

关键词： Wireless networks

CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

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International Journal of Hybrid Intelligent Systems 2021年第1-2期17卷 71-85页

作者： Bharati, Subrato Podder, Prajoy Mondal, M. Rubaiyat Hossain Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati OH United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH United States

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia. © 2021 - IOS Press. All rights reserved.

关键词： CO-ResNet coronavirus COVID-19 deep learning ResNet X-ray

Frequency multiplexed photonic reservoir computing using a Mach-Zehnder interferometer

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Frequency multiplexed photonic reservoir computing using a M...

2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Cuevas, Jonathan Uchida, Atsushi Minoshima, Kaoru Kuse, Naoya Graduate School of Sciences and Technology for Innovation Tokushima University 2-1 Minami-Josanjima Tokushima Tokushima770-8506 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-okubo Sakura-ku Saitama Saitama City338-8570 Japan Institute of Post-LED Photonics Tokushima University 2-1 Minami-Josanjima Tokushima Tokushima770-8506 Japan Graduate School of Informatics and Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan

We demonstrate a frequency-multiplexed photonic reservoir computing with nodes from a frequency comb and Mach-Zehnder interferometer as a reservoir, showcasing with the prediction of the Santa Fe time series data. ... 详细信息

关键词： Mach-Zehnder interferometers

Scintillation event imaging with a single photon avalanche diode camera

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Communications Engineering

Communications engineering 2024年第1期3卷 135页

作者： Bocchieri, Alex Charbon, Edoardo Velten, Andreas Department of Computer Sciences University of Wisconsin - Madison Madison WI United States Advanced Quantum Architecture Laboratory (AQUA) Swiss Federal Institute of Technology Lausanne (EPFL) Neuchâtel Switzerland Department of Biostatistics and Medical Informatics University of Wisconsin - Madison Madison WI United States Department of Electrical and Computer Engineering University of Wisconsin - Madison Madison WI United States

Position and time measurements of scintillation events encode information about the radiation source. Single photon avalanche diode (SPAD) arrays offer multiple-megapixel spatial resolution and tens of picoseconds temporal resolution for detecting single photons. Current lensless designs for measuring scintillation events use sensors that are lower in spatial resolution. Camera-based designs use sensors that are lower in temporal resolution or readout rate and cannot image individual interactions. Here we propose to image scintillation events in a thick, monolithic scintillator using a high-resolution SPAD camera. We demonstrate that a commercial SPAD camera is able to gather sufficient signal to image individual scintillation events and observe 3D shifts in their spatial distribution. Simulations show that a SPAD camera can localize individual scintillation events in 3D. We report direct imaging of gamma-ray interactions in a scintillator with a SPAD camera. The proposed design may allow to measure complex signatures of individual particles interacting in the scintillator. © The Author(s) 2024.

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Cognitive radio Technique for Blockchain-based Vehicles with IoT

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Cognitive Radio Technique for Blockchain-based Vehicles with...

International Conference on Contemporary Computing and informatics (IC3I)

作者： Sampenga Veerraju G S Pradeep Ghantasala Ekta Verma Ajay Kumar Shaik Vaseem Akram Kumud Pant Department of Electronics and Communication Engineering Dadi Institute of Engineering and Technology Anakapalle Andhra Pradesh India Department of Computer Science and Engineering Chitkara Institute of Engineering and Technology Chitkara University Punjab India Scientific Officer National Informatics Center New Delhi India Computer Science and Engineering JECRC University Jaipur Rajasthan India Uttaranchal Institute of Technology Uttaranchal University Dehradun Uttarakhand India Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

In the In the Internet of Vehicles (IoV), intelligent decision-making is performed by objects with cognition. by comprehending both the social and physical worlds, Cognition radio Network (CRN) is seen as a workable option. The existence of dangerous devices (MD) further impairs network performance, although For services to be improved and safety metrics to be driven, spectrum availability and information exchange among vehicles are crucial. Recently, a blockchain approach has been added to CRN-based IoV to stop data modification from these Entities and enable the cars to monitor both legal and illegitimate network activity. In this paper, we provide security to IoV throughout spectrum sensors and details transfer using CRN by sensing the paths through with a decision-making technique called Method for Order Personal taste by Similarity to a Solution (topsis (TOPSIS), a technique that inspires the confidence of its Behavioral Users (CU) by analysing certain predefined attributes. The network also maintains a blockchain to track every action involving information that has been saved. Using different frequency sensing and security settings in comparison to an IoV baseline solution, the suggested method is thoroughly evaluated against a number of security criteria. Numerous simulations indicate that when compared to the baseline system, our suggested approach is around 70% more effective at identifying malicious nodes and DoS threats.

关键词： Performance evaluation Decision making Sensor phenomena and characterization Cognition Blockchains Safety Automobiles

KNOWLEDGE-BASED DATASET FOR TraINING PE MALWARE DETECTION MODELS

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arXiv 2022年

作者： Švec, Peter Balogh, Štefan Homola, Martin Kl’uka, Ján Institute of Computer Science and Mathematics Faculty of Electrical Engineering and Information Technology Slovak University of Technology Ilkovičova 3 Bratislava Slovakia Department of Applied Informatics Faculty of Mathematics Physics and Informatics Comenius University Mlynská dolina Bratislava Slovakia

Ontologies are a standard for semantic schemata in many knowledge-intensive domains of human interest. They are now becoming increasingly important also in areas until very recently dominated by subsymbolic representations and machine-learning-based data processing. One such area is information security, and more specifically malware detection. We propose PE Malware Ontology that offers a reusable semantic schema for Portable Executable (PE, Windows binary format) malware files. The ontology was inspired by the structure of the data in the EMBER dataset and it currently covers the data intended for static malware analysis. With this proposal, we hope to achieve: a) a unified semantic representation for PE malware datasets that are available or will be published in the future;(b) applicability of symbolic, neural-symbolic, or otherwise explainable approaches in the PE Malware domain that may lead to improved interpretability of results which may now be characterized by the terms defined in the ontology;and (c) by joint publishing of semantically treated EMBER data, including fractional datasets, also improved reproducibility of experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Ontology

Tele-Rheumatology-Advanced Neural Network Model for Remote Assessment and Management of Rheumatic Conditions

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Tele-Rheumatology-Advanced Neural Network Model for Remote A...

International Conference on Communications and Signal Processing

作者： S. Aghalya R. Sarasu Nilamadhab Mishra G. Arunachalam D. Gobinath Suresh Babu Thandullu Naganathan Department of Electronics and Communication Engineering St. Joseph’s College of Engineering OMR Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering Gnanamani College of Technology (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority.

关键词： Ethics Technological innovation Biological system modeling Neural networks Medical treatment Signal processing Sensor fusion

An algorithmic study in the vector model for wireless power transfer maximization

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arXiv 2021年

作者： Katsidimas, Ioannis Nikoletseas, Sotiris raptis, Theofanis P. raptopoulos, Christoforos Institute of Informatics and Telematics National Research Council Pisa Italy Department of Computer Engineering and Informatics University of Patras Patras Greece Computer Technology Institute and Press Diophantus Patras Greece

rapid technological advances in the domain of Wireless Power Transfer (WPT) pave the way for novel methods for power management in systems of wireless devices and recent research works have already started considering algorithmic solutions for tackling emerging problems. However, many of those works are limited by the system modelling, and more specifically the one-dimensional abstraction suggested by Friis formula for the power received by one antenna under idealized conditions given another antenna some distance away. Different to those works, we use a model which arises naturally from fundamental properties of the superposition of energy fields. This model has been shown to be more realistic than other one-dimensional models that have been used in the past and can capture superadditive and cancellation effects. Under this model, we define two new interesting problems for configuring the wireless power transmitters so as to maximize the total power in the system and we prove that the first problem can be solved in polynomial time. We present a distributed solution that runs in pseudo-polynomial time and uses various knowledge levels and we provide theoretical performance guarantees. Finally, we design three heuristics for the second problem and evaluate them via simulations. © 2021, CC BY-NC-ND.

关键词： Energy transfer

Wireless charging for weighted energy balance in populations of mobile peers

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arXiv 2021年

作者： Nikoletseas, Sotiris raptis, Theofanis P. raptopoulos, Christoforos Institute of Informatics and Telematics National Research Council Pisa Italy Department of Computer Engineering and Informatics University of Patras Patras Greece Computer Technology Institute and Press "Diophantus" Patras Greece

Wireless energy transfer is an emerging technology that is used in networks of battery-powered devices in order to deliver energy and keep the network functional. Existing state-of-the-art studies have mainly focused on applying this technology on networks of relatively strong computational and communicational capabilities (wireless sensor networks, ad-hoc networks);also they assume energy transfer from special chargers to regular network nodes. Different from these works, we study how to efficiently transfer energy wirelessly in populations of battery-limited devices, towards prolonging their lifetime. In contrast to the state-of-the-art, we assume a much weaker population of distributed devices which are exchanging energy in a "peer to peer" manner with each other, without any special charger nodes. We address a quite general case of diverse energy levels and priorities in the network and study the problem of how the system can efficiently reach a weighted energy balance state distributively, under both loss-less and lossy power transfer assumptions. Three protocols are designed, analyzed and evaluated, achieving different performance trade-offs between energy balance quality, convergence time and energy efficiency. © 2021, CC BY-NC-ND.

关键词： Energy balance