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Underwater Communications and Networking (UComms)

作者： Mandar Chitre Acoustic Research Laboratory Tropical Marine Science Institute Department of Electrical and Computer Engineering National University of Singapore

ISBN: (数字)9798331542511

ISBN: (纸本)9798331542528

Underwater acoustic communication algorithms typically undergo numerical testing within simulated environments prior to real-world application. These simulations require noise samples that accurately reflect the ambient noise in the underwater environment. The common practice of employing a white Gaussian noise model for additive noise in simulations fails to capture the true nature of under-water noise. Given the wideband nature of most underwater communication systems and the frequency-dependence of ambient noise, the assumption of white noise becomes untenable. Furthermore, communication systems equipped with multiple hydrophones for spatial diversity experience correlated noise across hy-drophones. This paper presents a simple technique for generating colored noise that is consistent with the spatial and temporal correlation characteristics of ambient noise measured at sea.

关键词： Underwater communication Spatial diversity Noise Sea measurements White noise Numerical models Underwater acoustics Colored noise Wideband Testing

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Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data

Implicit Neural Representations for Simultaneous Reduction a...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Alif Bin Abdul Qayyum Xihaier Luo Nathan M. Urban Xiaoning Qiari Byung-Iun Yoon Department of Electrical and Computer Engineering Texas A&M University Computational Science Initiative Brookhaven National Laboratory

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce green-house gas emissions that contribute to global warming. To enhance the analysis and storage of wind data, we introduce a deep learning framework designed to simultaneously enable effective dimensionality reduction and continuous representation of multi-altitude wind data from discrete observations. The framework consists of three key components: dimensionality reduction, cross-modal prediction, and super-resolution. We aim to: (1) improve data resolution across diverse climatic conditions to recover high-resolution details; (2) reduce data dimensionality for more efficient storage of large climate datasets; and (3) enable cross-prediction between wind data measured at different heights. Comprehensive testing confirms that our approach surpasses existing methods in both super-resolution quality and compression efficiency.

关键词： Dimensionality reduction Deep learning Wind energy generation Extrapolation Wind energy Wind speed Superresolution Wind farms Data models Testing Climate change Renewable energy sources

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Stability of Traveling Waves Solutions for Nonlinear Cellular Neural Networks with Distributed Delays

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Journal of Systems science & Complexity 2022年第1期35卷 18-31页

作者： GUO Yingxin GE Shuzhi Sam ARBI Adnène School of Mathematical Sciences Qufu Normal UniversityQufu 273165China Department of Electrical and Computer Engineering National University of Singapore117576Singapore National School of Advanced Sciences and Technologies of Borj Cedria University of CarthageEl Khawarizmi StreetCarthage 2078Tunisia Laboratory of Engineering Mathematics(LR01ES13) Tunisia.

This paper investigates the exponential stability of traveling wave solutions for nonlinear delayed cellular neural *** a continuity of the past work(Wu and Niu,2016;Yu,et al.,2011)on the existence and uniqueness of the traveling wave solutions,it is very reasonable and interesting to consider the exponential stability of the traveling wave *** the weighted energy method,comparison principle and the first integral mean value theorem,this paper proves that,for all monotone traveling waves with the wave speed cc2*>0,the solutions converge time-exponentially to the corresponding traveling waves,when the initial perturbations decay at some fields.

关键词： Cellular delayed neural networks comparison principle stability analysis traveling waves solutions

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Deep Learning Based Uplink Precoding for High Speed Train Communications in FD-RAN

Deep Learning Based Uplink Precoding for High Speed Train Co...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zeyu Sun Jiwei Zhao Yunting Xu Jiacheng Chen Haibo Zhou Lian Zhao School of Electronic Science and Engineering Nanjing University Information Science and Electronic Engineering Zhejiang University Department of Strategic and Advanced Interdisciplinary Research Peng Cheng Laboratory Department of Electrical Computer Biomedical Engineering Toronto Metropolitian University

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

High speed train (HST) communications with multi-user multiple-input multiple-output (MU-MIMO) techniques have shown great potential in system performance improvements. However, the challenges caused by higher pilot overhead and the belated channel state information (CSI) feedback in such high mobility scenarios still need to be further addressed. To this end, fully-decoupled radio access network (FD-RAN) with novel location-based feedback-free transmission scheme and cooperative transmission/reception in separated downlink/uplink networks is regarded as a promising solution. In this paper, we study the uplink precoding design in FD-RAN for HST communications. To capture the inherent relation between location and precoding, the line-of-sight (LoS) channel is derived from location and then fed into a proposed precoding design neural network (PDNN) which learns to jointly optimize the precoding scheme for all the multi-antenna mobile relays (MRs) in uplink MU-MIMO. We adopt a custom loss function to optimize the spectrum efficiency (SE). Moreover, a joint signal reception method is given, also based solely on the LoS channel derived from location, so as to avoid frequent pilot transmission. Simulation results show the advantages of FD-RAN against other architectures and demonstrate that our proposed PDNN achieves better performance than traditional precoding scheme in high mobility scenarios.

关键词： Vehicular and wireless technologies Precoding OFDM System performance Simulation Neural networks Rician channels

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Measurement of Synthetic-Dimension Band Structures in Silicon Coupled Ring Resonators 16

Measurement of Synthetic-Dimension Band Structures in Silico...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Zhang, Ruming Li, Liucun Kamata, Mikiya Baba, Toshihiko Ozawa, Tomoki Ota, Yasutomo Iwamoto, Satoshi Research Center for Advanced Science and Technology The University of Tokyo Tokyo153-8904 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8533 Japan

We fabricated integrated silicon coupled-ring resonators and measured photonic band structures for synthetic frequency lattices with different configurations. A phase difference between two modulation signals introduc... 详细信息

ISBN: (纸本)9798350372076

关键词： Silicon photonics

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Semantic Communication in Multi-team Dynamic Games: A Mean Field Perspective

arXiv

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arXiv 2024年

作者： Aggarwal, Shubham Zaman, Muhammad Aneeq uz Bastopcu, Melih Başar, Tamer United States The Coordinated Science Laboratory UIUC United States The Coordinated Science Laboratory The Department of Electrical and Computer Engineering UIUC United States

Coordinating communication and control is a key component in the stability and performance of networked multi-agent systems. While single user networked control systems have gained a lot of attention within this domain, in this work, we address the more challenging problem of large population multi-team dynamic games. In particular, each team constitutes two decision makers (namely, the sensor and the controller) who coordinate over a shared network to control a dynamically evolving state of interest under costs on both actuation and sensing/communication. Due to the shared nature of the wireless channel, the overall cost of each team depends on other teams’ policies, thereby leading to a noncooperative game setup. Due to the presence of a large number of teams, we compute approximate decentralized Nash equilibrium policies for each team using the paradigm of (extended) mean-field games, which is governed by (1) the mean traffic flowing over the channel, and (2) the value of information at the sensor, which highlights the semantic nature of the ensuing communication. In the process, we compute optimal controller policies and approximately optimal sensor policies for each representative team of the mean-field system to alleviate the problem of general non-contractivity of the mean-field fixed point operator associated with the finite cardinality of the sensor action space. Consequently, we also prove the ǫ–Nash property of the mean-field equilibrium solution which essentially characterizes how well the solution derived using mean-field analysis performs on the finite-team system. Finally, we provide extensive numerical simulations, which corroborate the theoretical findings and lead to additional insights on the properties of the results presented. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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advanced Schedulability Testing for Adaptive Mixed-Criticality Systems under Weakly Hard Real-Time Constraints

Advanced Schedulability Testing for Adaptive Mixed-Criticali...

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CSI Symposium on Real-Time and Embedded Systems and Technologies (RTEST)

作者： Hossein Rabbiun Jamal Mohammadi Mahmoud Shirazi Mehdi Kargahi Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529284

ISBN: (纸本)9798331529291

This paper advances the schedulability analysis of the Adaptive Mixed-Criticality for Weakly Hard Real-Time Systems (AMC-WH) which allows a specified number of consecutive low-criticality (LO) jobs of tasks to be skipped when the system operates in the high-criticality (HI) mode. The current AMC-WH schedulability test, based on the skip-over model, in which low tasks are guaranteed to meet m − s out of m consecutive deadlines, where s is the number of allowed skips and m is the cycle length, only calculates the worst-case response times (WCRT) for low tasks under a fixed execution pattern post-mode switch. To address this limitation, we propose a new sufficient schedulability test for AMC-WH using the (m,k)-firm model, which allows various execution patterns for low tasks after a mode switch, enhancing the flexibility and applicability. Additionally, we develop an exact schedulability test for AMC-WH based on response time analysis (RTA). This test modifies the existing exact tests for the AMC algorithm, incorporating updates to state-space exploration and pruning rules to account for low task execution after a mode switch. Simulation results validate the effectiveness of our tests, demonstrating enhanced schedulability analysis and optimized resource allocation.

关键词： Adaptation models Adaptive systems Scheduling algorithms Simulation Switches Cyber-physical systems Real-time systems Time factors Resource management Testing

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Latest Innovations in Intelligent Network-on-Chip architectures: A Systematic Review

Latest Innovations in Intelligent Network-on-Chip Architectu...

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International Workshop on Network on Chip architectures, NoCArc

作者： Elena Kakoulli Department of Computer Science Intelligent Systems Laboratory Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798331506421

ISBN: (纸本)9798331506438

This review investigates the latest advancements in intelligent Network-on-Chip (NoC) architectures, focusing on innovations from 2022 to 2024. The integration of Artificial Intelligence (AI) and Machine Learning (ML) into NoC designs has driven significant improvements in performance, scalability, energy efficiency, and security. Key contributions include AI/ML-driven techniques to optimize routing, reduce latency, manage power consumption, and enhancing security against emerging threats. Despite these advancements, challenges such as computational overhead and integration complexities remain. This review consolidates these findings, addresses existing challenges, and proposes future research directions to further leverage AI and ML for developing robust and adaptable NoC architectures.

关键词： Technological innovation Scalability Power system management computer architecture Network-on-chip Predictive models Routing Energy efficiency Complexity theory Security

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Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach

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IEEE Transactions on Wireless Communications 2023年第3期22卷 1808-1822页

作者： He, Hengtao Wang, Rui Jin, Weijie Jin, Shi Wen, Chao-Kai Li, Geoffrey Ye Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Electronic and Information Engineering Hong Kong Hong Kong Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2BX United Kingdom Southeast University National Mobile Communications Research Laboratory Nanjing210096 China

Millimeter-wave (mmWave) communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in mmWave band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the spatial-wideband effect in wideband mmWave systems makes channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the mmWave system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs. © 2002-2012 IEEE.

关键词： Channel estimation

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Highly ordered inkjet-printed quantum-dot thin films enable efficient and stable QLEDs with EQE exceeding 23%

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escience 2024年第3期4卷 152-162页

作者： Changting Wei Bo Xu Meng Zhang Zhenhuang Su Jiawei Gu Wenrui Guo Xingyu Gao Wenming Su Zheng Cui Seokwoo Jeon Zhiyong Fan Haibo Zeng MIIT Key Laboratory of Advanced Display Materials and Devices Institute of Optoelectronics&NanomaterialsSchool of Materials Science and EngineeringNanjing University of Science and TechnologyNanjing 210094China Ningbo Nato New Material Technology Co. LtdNingbo 315600China Shanghai Synchrotron Radiation Facility(SSRF) Zhangjiang LaboratoryShanghai Advanced Research InstituteChinese Academy of SciencesShanghai 201204China School of Mechanical Engineering Jiangsu UniversityZhenjiang 212013China Printable Electronics Research Center Nano Devices and Materials DivisionSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Department of Materials Science and Engineering Korea UniversitySeoul 02841South Korea Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China

Inkjet-printed quantum dot light-emitting diodes(QLEDs)are emerging as a promising technology for next-generation ***,the progress in fabricating QLEDs using inkjet printing technique has been slower compared to spin-coated devices,particularly in terms of efficiency and *** key to achieving high performance QLEDs lies in creating a highly ordered and uniform inkjet-printed quantum dot(QD)thin *** this study,we present a highly effective strategy to significantly improve the quality of inkjet-printed CdZnSe/CdZnS/ZnS QD thin films through a pressure-assisted thermal annealing(PTA)*** from this PTA process,a high quality QD thin film with ordered packing,low surface roughness,high photoluminescence and excellent electrical property is *** mechanism behind the PTA process and its profound impact on device performance have been thoroughly investigated and ***,a record high external quantum efficiency(EQE)of 23.08%with an impressive operational lifetime(T50)of up to 343,342h@100cdm−2,and a record EQE of 22.43%with T50 exceeding to 1,500,463h@100cdm−2 are achieved in inkjet-printed red and green CdZnSe-based QLEDs,*** work highlights the PTA process as an important approach to realize highly efficient and stable inkjet-printed QLEDs,thus advancing QLED technology to practical applications.

关键词： Inkjet printing Quantum dot light-emitting diode High efficiency Thermal annealing Highly ordered

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