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arXiv 2021年

作者： Lin, Chia-Hung Lin, Shih-Chun Blasch, Erik Intelligent Wireless Networking Laboratory Department of Electrical and Computer Engineering North Carolina State University RaleighNC27695 United States Air Force Office of Scientific Research ArlingtonVA22203 United States

Due to spectrum scarcity and increasing wireless capacity demands, terahertz (THz) communications at 0.1-10THz and the corresponding spectrum characterization have emerged to meet diverse service requirements in future 5G and 6G wireless systems. However, conventional compressed sensing techniques to reconstruct the original wideband spectrum with under-sampled measurements become inefficient as local spectral correlation is deliberately omitted. Recent works extend communication methods with deep learning-based algorithms but lack strong ties to THz channel properties. This paper introduces novel THz channel-aware spectrum learning solutions that fully disclose the uniqueness of THz channels when performing such ultra-broadband sensing in vehicular environments. Specifically, a joint design of spectrum compression and reconstruction is proposed through a structured sensing matrix and two-phase reconstruction based on high spreading loss and molecular absorption at THz frequencies. An end-to-end learning framework, namely compression and reconstruction network (CRNet), is further developed with the mean-square-error loss function to improve sensing accuracy while significantly reducing computational complexity. Numerical results show that the CRNet solutions outperform the latest generative adversarial network (GAN) realization with a much higher cosine and structure similarity measures, smaller learning errors, and 56\% less required training overheads. This THz Ultra-broadband Learning Vehicular Channel-Aware networking (TULVCAN) work successfully achieves effective THz spectrum learning and hence allows frequency-agile access. Copyright © 2021, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Multi-Slot Distributed Measurement Selection: A Sparsity Learning Approach

Multi-Slot Distributed Measurement Selection: A Sparsity Lea...

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作者： Xia, Qian Wang, Wei Ran, Rong Gong, Yi Zhang, Zhaoyang College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Computer Engineering Ajou University Suwon16499 Korea Republic of Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China

When only a part of measurements are collected sequentially in large-scale Internet of Things (IoT) networks, it is motivated to extract significant information from the collected data for utilizing the communication resources over incoming slots more efficiently. In this article, we study the successive measurement selection problem for sparse parameter estimation and its distributed implementation in large-scale networks. Specifically, we transform the intractable multi-slot measurement selection problem into per-slot subproblems, and in each slot we select the uncollected measurements that are more 'useful' for parameter estimation based on sparse Bayesian learning. In order to deal with the combinatorial problem with distributed implementation, we relax the subproblem and propose an enhanced subgradient method to solve the Boolean constraint. The proposed distributed scheme needs only a common information broadcast by the centralized controller, where the communication overheads only depend on the number of sparse active sources rather than the number of sensors which is always huge. Simulation results demonstrate the performance gain of our proposed measurement selection method compared to the conventional schemes. When the total communication overheads are limited with the network scale, our scheme presents extreme practicability and applicability for the large-scale networks. © 2015 IEEE.

关键词： Wireless sensor networks

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Multiple-Image Super-Resolution for Networked Extremely Low-Resolution Thermal Sensor Array

Multiple-Image Super-Resolution for Networked Extremely Low-...

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Machine Learning on Edge in Sensor Systems (SenSys-ML), IEEE Second Workshop on

作者： Chi-Sheng Shih Yao-Ting Wang Jyun-Jhe Chou Department of Computer Science and Information Engineering Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan

ISBN: (数字)9781728199962

ISBN: (纸本)9781728199979

Observing activeness of patients and elderly for long time at private space is a desired metric for elderly care. Many state-of-the-art mechanisms require either using wearable sensors, assists by medical staffs, or observation at designated location. Consequently, it is difficult, if not impossible, to acquire long term trend. Thermal sensors are not sensitive to illumination and can detect heat sources under low lightness. Moreover, it is not trivial to identify an individual from thermal images. However, a low cost and effective solution remain open. Low-resolution thermal sensors cost less but suffer from noisy reading. In this work, we fuse multiple low-resolution thermal images to reconstruct higher resolution thermal images to identify human activities. The evaluation results show that the accuracy of activity recognition while using low resolution thermal images are 96% of that while using high resolution thermal images, which requires 80 times of data and ten times of hardware cost.

关键词： Sensor arrays THERMAL SENSOR Medical Staff Thermal Personal Space Wearable sensors images Heat sources super-resolution aged person High definition Human Activities illumination Lighting

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Maximum Sub-array Diversity for mmWave Network under RF Power Leakage and Transceiver Distortion Noises

Maximum Sub-array Diversity for mmWave Network under RF Powe...

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IEEE Conference on Wireless Communications and networking

作者： Leila Tlebaldiyeva Behrouz Maham Olav Tirkkonen Department of Electrical and Computer Engineering Nazarbayev University Nur-Sultan Kazakhstan Department of Communications and Networking Aalto University Aalto Finland

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

This paper investigates RF power leakage in millimeter wave (mmWave) networks operating on hybrid beamforming (HB) system where a base station with massive MIMO antennas communicates with user equipment (UE) nodes equipped with a single antenna. RF power leaks between spatially divided transmissions to different users, due to back/side lobes of antennas. A maximum sub-array transmission diversity technique implemented on HB is proposed to improve the system performance under RF power leakage and residual transceiver distortion noise. In this work, we emphasize how RF power leakage and residual transceiver distortion noise constraints degrade the quality of communication performance in terms of outage probability (OP) and ergodic capacity. An analytic model of mmWave connectivity is used, resulting in closed-form expressions for the OP and ergodic capacity. These are corroborated through Monte-Carlo simulations. Simulation results demonstrate that the effect of transceiver distortion noise is more severe at high signal power due to the proportionality of transceiver distortion noise to signal power.

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Telecom networking with a diamond quantum memory

arXiv

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arXiv 2023年

作者： Bersin, Eric Sutula, Madison Huan, Yan Qi Suleymanzade, Aziza Assumpcao, Daniel R. Wei, Yan-Cheng Stas, Pieter-Jan Knaut, Can M. Knall, Erik N. Langrock, Carsten Sinclair, Neil Murphy, Ryan Riedinger, Ralf Yeh, Matthew Xin, C.J. Bandyopadhyay, Saumil Sukachev, Denis D. MacHielse, Bartholomeus Levonian, David S. Bhaskar, Mihir K. Hamilton, Scott Park, Hongkun Lončar, Marko Fejer, Martin M. Dixon, P. Benjamin Englund, Dirk R. Lukin, Mikhail D. Lincoln Laboratory Massachusetts Institute of Technology LexingtonMA02421 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Harvard University CambridgeMA02138 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States E. L. Ginzton Laboratory Stanford University StanfordCA94305 United States Aws Center for Quantum Networking BostonMA02135 United States Department of Chemistry and Chemical Biology Harvard University CambridgeMA02138 United States

Practical quantum networks require interfacing quantum memories with existing channels and systems that operate in the telecom band. Here we demonstrate low-noise, bidirectional quantum frequency conversion that enables a solid-state quantum memory to directly interface with telecom-band systems. In particular, we demonstrate conversion of visible-band single photons emitted from a silicon-vacancy (SiV) center in diamond to the telecom O-band, maintaining low noise (g2(0) © 2023, CC BY-NC-ND.

关键词： Diamonds

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Converged reconfigurable intelligent surface and mobile edge computing for space information networks

arXiv

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arXiv 2021年

作者： Cao, Xuelin Yang, Bo Huang, Chongwen Yuen, Chau Zhang, Yan Niyato, Dusit Han, Zhu Engineering Product Development Pillar Singapore University of Technology and Design Singapore Singapore College of Information Science and Electronic Engineering Zhejiang Provincial Key Lab of information processing communication and networking International Joint Innovation Center Zhejiang University China Department of Informatics University of Oslo Norway School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering University of Houston United States

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing

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Quantum Computing: Vision and Challenges

arXiv

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arXiv 2024年

作者： Gill, Sukhpal Singh Cetinkaya, Oktay Marrone, Stefano Claudino, Daniel Haunschild, David Schlote, Leon Wu, Huaming Ottaviani, Carlo Liu, Xiaoyuan Machupalli, Sree Pragna Kaur, Kamalpreet Arora, Priyansh Liu, Ji Farouk, Ahmed Song, Houbing Herbert Uhlig, Steve Ramamohanarao, Kotagiri School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Dipartimento di Matematica e Fisica Università della Campania "Luigi Vanvitelli" Italy Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN United States Detecon International GmbH Munich Germany DB Cargo Berlin Germany Center for Applied Mathematics Tianjin University Tianjin China Department of Computer Science York Centre for Quantum Technologies University of York York United Kingdom Fujitsu Research of America Inc. Santa ClaraCA United States Information Networking Institute Carnegie Mellon University United States Cymax Group Technologies BC Canada Microsoft Schiphol Netherlands Mathematics and Computational Research Division Argonne National Laboratory IL United States Department of Computer Science Faculty of Computers and Artificial Intelligence South Valley University Hurghada Egypt Baltimore United States The University of Melbourne VIC Australia

The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex problems that cannot be solved otherwise with conventional computing methods. These problems include modeling quantum mechanics, logistics, chemical-based advances, drug design, statistical science, sustainable energy, banking, reliable communication, and quantum chemical engineering. The last few years have witnessed remarkable progress in quantum software and algorithm creation and quantum hardware research, which has significantly advanced the prospect of realizing quantum computers. It would be helpful to have comprehensive literature research on this area to grasp the current status and find outstanding problems that require considerable attention from the research community working in the quantum computing industry. To better understand quantum computing, this paper examines the foundations and vision based on current research in this area. We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers. Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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Evaluating Oil Spill Contamination around Qaruh Island, Kuwait: Insights from EOS-04 C-Band SAR Data

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Regional Studies in Marine Science 2025年

作者： N Krishnaraj Mohammad Alamgeer Shouki A Ebad Haya Mesfer Alshahrani Saraswathi A Sumaiya Begum J Jayanthi Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Information Systems College of Science & Art at Mahayil King Khalid University Saudi Arabia Department of Computer Science Faculty of Science Northern Border University Arar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D.Engineering College Tamil Nadu India Department of Computer Science and Engineering Sona College of Technology Salem Tamil Nadu India

Oil spills threaten marine environments, especially in ecologically sensitive regions like Qaruh Island, Kuwait. This study investigates the extent and severity of oil contamination surrounding Qaruh Island by leveraging C-Band Synthetic Aperture Radar (SAR) data acquired from two key satellite sources, Sentinel-1A and EOS-04, on April 23, 2024. Both satellites provided dual-polarized SAR data (HH and VV), which were analysed to enhance oil spill detection through polarization-based discrimination techniques. The analysis compared backscatter values from oil-covered and clean water surfaces to delineate the spill area accurately. Sentinel-1A and EOS-04 SAR data were pre-processed for geometric and radiometric corrections, followed by the application of filtering techniques to mitigate speckle noise and enhance detection clarity. Through this approach, we successfully detected a continuous oil spill extending over an area of approximately 20 square kilometres. The HH polarization was particularly effective in identifying the boundaries of the spill, while VV polarization contributed to confirming the presence of oil by highlighting differences in surface roughness between contaminated and uncontaminated areas. The results demonstrate that dual-polarized SAR data, specifically C-Band data, are crucial in oil spill monitoring, providing timely and accurate contamination assessments in coastal and marine environments. By utilizing Sentinel-1A and EOS-04 SAR datasets, this study underscores the effectiveness of multi-source SAR data for rapid response in environmental monitoring, ultimately supporting resource protection and mitigation efforts around Qaruh Island.

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Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans

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Journal of Healthcare engineering 2022年第1期2022卷 3861161页

作者： Alzu'Bi, Dalia Abdullah, Malak Hmeidi, Ismail Alazab, Rami Gharaibeh, Maha El-Heis, Mwaffaq Almotairi, Khaled H. Forestiero, Agostino Hussein, Ahmad Mohdaziz Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid2210 Jordan Department of General Surgery and Urology University of Science and Technology Irbid22110 Jordan Department of Diagnostic and Interventional Radiology Faculty of Medicine Jordan University of Science and Technology Irbid2210 Jordan Computer Engineering Department Computer and Information Systems College Umm Al-Qura University Makkah21955 Saudi Arabia Institute for High Performance Computing and Networking CNR CS Rende Italy Deanship of E-Learning and Distance Education Umm Al-Qura University Makkah21955 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Faculty of Information Technology Middle East University Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures that reduce effects, and overcoming the tumor. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of deep learning (DL) can save diagnosis time, improve test accuracy, reduce costs, and reduce the radiologist's workload. In this paper, we present detection models for diagnosing the presence of KTs in computed tomography (CT) scans. Toward detecting and classifying KT, we proposed 2D-CNN models;three models are concerning KT detection such as a 2D convolutional neural network with six layers (CNN-6), a ResNet50 with 50 layers, and a VGG16 with 16 layers. The last model is for KT classification as a 2D convolutional neural network with four layers (CNN-4). In addition, a novel dataset from the King Abdullah University Hospital (KAUH) has been collected that consists of 8,400 images of 120 adult patients who have performed CT scans for suspected kidney masses. The dataset was divided into 80% for the training set and 20% for the testing set. The accuracy results for the detection models of 2D CNN-6 and ResNet50 reached 97%, 96%, and 60%, respectively. At the same time, the accuracy results for the classification model of the 2D CNN-4 reached 92%. Our novel models achieved promising results;they enhance the diagnosis of patient conditions with high accuracy, reducing radiologist's workload and providing them with a tool that can automatically assess the condition of the kidneys, reducing the risk of misdiagnosis. Furthermore, increasing the quality of healthcare service and early detection can change the disease's track and preserve the patient's life. © 2022 Dalia Alzu'bi et al.

关键词： Convolutional neural networks

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DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialogue

arXiv

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arXiv 2025年

作者： Li, Xiang Pan, Duyi Xiao, Hongru Han, Jiale Tang, Jing Ma, Jiabao Wang, Wei Cheng, Bo State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Department of Computer Science and Engineering Hong Kong University of Science and Technology Guangzhou China Hong Kong University of Science and Technology Hong Kong

Speech synthesis is crucial for human-computer interaction, enabling natural and intuitive communication. However, existing datasets involve high construction costs due to manual annotation and suffer from limited character diversity, contextual scenarios, and emotional expressiveness. To address these issues, we propose DialogueAgents, a novel hybrid agent-based speech synthesis framework, which integrates three specialized agents—a script writer, a speech synthesizer, and a dialogue critic—to collaboratively generate dialogues. Grounded in a diverse character pool, the framework iteratively refines dialogue scripts and synthesizes speech based on speech review, boosting emotional expressiveness and paralinguistic features of the synthesized dialogues. Using DialogueAgent, we contribute MultiTalk, a bilingual, multi-party, multi-turn speech dialogue dataset covering diverse topics. Extensive experiments demonstrate the effectiveness of our framework and the high quality of the MultiTalk dataset. We release the dataset and code1 to facilitate future research on advanced speech synthesis models and customized data generation. Copyright © 2025, The Authors. All rights reserved.

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