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International Journal of Interactive Mobile Technologies 2019年第10期13卷 188-197页

作者： Hussin, Masnida Mahmood, Raja Azlina Raja Mustaffa, Mas Rina Department of Communication Technology and Networks Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Malaysia

Energy consumption in distributed computing system gains a lot of attention recently after its processing capacity becomes significant for better business and economic operations. Comprehensive analysis of energy efficiency in high-performance data center for distributed processing requires ability to monitor a proportion of resource utilization versus energy consumption. In order to gain green data center while sustaining computational performance, a model of energy efficient cyber-physical communication is proposed. A real-time sensor communication is used to monitor heat emitted by processors and room temperature. Specifically, our cyber-physical communication model dynamically identifies processing states in data center while implying a suitable air-conditioning temperature level. The information is then used by administration to fine-tune the room temperature according to the current processing activities. Our automated triggering approach aims to improve edge computing performance with cost-effective energy consumption. Simulation experiments show that our cyber-physical communication achieves better energy consumption and resource utilization compared with other cooling model. © 2019 International Association of Online Engineering.

关键词： Energy utilization

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Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Multi-User communications

Rate-Overhead Tradeoff in Beam Training for RRS-Assisted Mul...

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IEEE Conference on Vehicular Technology (VTC)

作者： Shupei Zhang Yutong Zhang Boya Di Hongliang Zhang School of Electronics Engineering and Computing Science Peking University Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering Princeton University NJ USA

ISBN: (纸本)9781665454698

Holographic multiple-input multiple-output (HMIMO) with a spatially continuous aperture is a promising solution for future radio access to handle the explosively increasing data demands. As a key enabler of HMIMO, the reconfigurable refractive surface (RRS) can serve as an antenna array with numerous programmable radiation elements. In this paper, we consider a multi-user system with an RRS-aided base station (BS) where the transmit signal is refracted by the RRS towards the users. A beamforming scheme is developed via codebook design and beam training. A larger codebook size implies more codewords, each corresponding to a directional beam. When the codebook size increases, the directivity of the refracted beam is enhanced, bringing a higher data rate. However, it also leads to an exponential growth of the training overhead. To achieve the critical tradeoff between the data rate and overhead, we evaluate the system throughput and model the relation between the codebook size of the RRS and the throughput mathematically. The optimal codebook size is then derived given different user distributions. Simulation results verify our theoretical analysis and show the influence of both codebook size and RRS size on the throughput.

关键词： Training Vehicular and wireless technologies Array signal processing Simulation System performance Transmitting antennas Throughput

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BD-WNet: Boundary Decoupling based W-shape Network for Road Segmentation in Optical Remote Sensing Imagery

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Fan, Shilong Xie, Jianlin Zhuang, Yin Chen, He Su, Zhibao Li, Lianlin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing 100081 China China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory Beijing 100072 China Peking University Schools of Computer Science and State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing 100871 China

Using very high-resolution optical remote sensing images for road segmentation is a challenging and important interpretation task. Different from other segmentation tasks, road segmentation typically faces unpredictable structure, irregular distribution, and complex background interference. Thus, establishing an effective and stable semantic description for road segmentation becomes a challenge. In this article, a novel architecture called Boundary Decoupling based W-shape Network (BD-WNet) is proposed for achieving road segmentation from optical remote sensing imagery. First, a novel W-shaped double encoder-decoder architecture network is designed to provide more stable semantic description, which can be used for road body extraction. Second, the unstable semantic features within the initial stage of double encoder-decoder architecture are considered for road boundary description. For decoupling unstable boundary information from the output feature of first encoder-decoder, a boundary separate model is designed, which is called Boundary-Body Decoupling (BBD) module. This module utilizes the flow field mechanism to compare image features before and after passing through the encoder-decoder. The stable features among the overall features are decoupled into the main body of the road, while the dynamic features are decoupled into the boundary of the road. Third, a boundary and body weighting fusion model is also designed to fuse stable road body and unstable road boundary information for supervised learning. Extensive experiments have been carried out on remote sensing road segmentation datasets, and our method achieves impressive performance. Specifically, the proposed BD-WNet achieves 82.5% F1 score and 69.8 IoU% on DeepGlobe dataset, 75.5% F1 score and 60.7% IoU on CHN6-CUG dataset and 93.8% F1 score and 88.3% IoU on Ottawa Road Dataset. © 2008-2012 IEEE.

关键词： Boundary extraction double encoding-decoding remote sensing semantic segmentation

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Fiber-delay-line-referenced optical frequency combs:three stabilization schemes

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Chinese Optics Letters 2022年第2期20卷 55-59页

作者： Kai Wang Haochen Tian Fei Meng Baike Lin Shiying Cao Yihan Pi Yan Han Zhanjun Fang Youjian Song Minglie Hu Ultrafast Laser Laboratory Key Laboratory of Opto-Electronic Information Technical Science of Ministry of EducationSchool of Precision Instrument and Opto-Electronics EngineeringTianjin UniversityTianjin 300072China Division of Time and Frequency Metrology National Institute of MetrologyBeijing 100029China Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China College of Physics and Optoelectronics Taiyuan University of TechnologyTaiyuan 030024China

We demonstrate the stabilization of an optical frequency comb(OFC) using a segment of fiber delay line as a reference. A mode-locked Er-doped fiber laser is phase locked to a kilometer-long fiber delay line using three different schemes. The short-term stability of the comb modes in the OFC stabilized by these schemes is obviously enhanced, down to the 10;level at millisecond average time. Among these three schemes, phase locking two bunches of comb modes in the OFC to the same fiber delay line exhibits the lowest residual phase noise. Fiber-delay-line-referenced OFCs can provide reliable laser sources in precise metrology owing to the advances of low cost, compactness, and high integration.

关键词： optical frequency comb delay line frequency noise

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2.4 GHz Wearable Textile Antenna/Rectenna for Simultaneous Information and Power Transfer

2.4 GHz Wearable Textile Antenna/Rectenna for Simultaneous I...

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European Conference on Antennas and Propagation, EuCAP

作者： Mahmoud Wagih Geoffrey S. Hilton Alex S. Weddell Steve Beeby School of Electronics and Computer Science University of Southampton U.K. Communication Systems & Networks Group University of Bristol U.K.

ISBN: (纸本)9788831299022;9781728188454

Antennas and rectennas for self-powered body area networks (BANs) have attracted significant interest, in an effort to improve the sustainability of e-textiles. This paper presents a novel dual-port fully-textile antenna, based on a simple microstrip patch, for simultaneous wireless information and power transfer (SWIPT) at 2.4 GHz, presenting the first antenna-rectifier co-design implementation for SWIPT. The proposed antenna's input bandwidth covers the license-free band achieving 8.9 dBi measured directivity and 41% efficiency at 2.4 GHz, with a simple proximity-coupled microstrip feed. For power harvesting, port 2 is designed to achieve a scalable complex impedance to directly match the rectifier without a separate matching network, and achieves 6.3 dBi off-body gain. The proposed rectenna feed improves the antenna's isolation by at least 15 dB compared to a microstrip feed similar to port 1. The rectenna achieves over 40% power conversion efficiency (PCE) from -10 dBm, when placed directly on-body, with a 71% peak PCE.

关键词： Antenna measurements Wireless communication Rectennas Microstrip antennas Bandwidth Antenna feeds Microstrip

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Negative Latency in computer Vision: A Key to Efficient Edge Offloading

Negative Latency in Computer Vision: A Key to Efficient Edge...

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IEEE Conference on Global communications (GLOBECOM)

作者： Jonas Schulz Susu Hu Stefanie Speidel Patrick Seeling Frank H. P. Fitzek Deutsche Telekom Chair of Communication Networks TU Dresden Germany Department of Translational Surgical Oncology NCT/UCC Dresden Faculty of Medicine University Hospital Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Centre for Tactile Internet With Human-in-the-Loop (CeTI) Department of Computer Science Central Michigan University USA Joint Project 6G-Life

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Object recognition tasks are commonplace in industrial and medical applications, but resource-limited end devices common to the Internet of Things (IoT) are limited in capabilities and commonly require offloading of compute-intensive tasks. We evaluate the negative latency concept as a new approach to image detection and object recognition applications in low-bandwidth scenarios without a feedback channel. We employ image quality assessment and risk estimators associated with image quality degradation for progressively transmitted images to realize negative latency. Our comprehensive evaluation on image recognition and object detection tasks shows that image quality assessment over progressively transmitted images can be enabled by using risk estimators associated with image quality degradation during progressive transmission. In turn, our approach provides the framework for service latency and resource use reductions.

关键词： Image quality Measurement Degradation computer vision Computational modeling Transform coding Object detection Encoding Internet of Things Object recognition

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Ensemble Learning-based Traffic Classification with Small-Scale Datasets for Wireless networks

Ensemble Learning-based Traffic Classification with Small-Sc...

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IEEE Conference on computer communications Workshops, INFOCOM Wksps

作者： Xiaorong Wang Wenting Wei Xiaoshan Yu Danyang Zheng Neeraj Kuma Lei Liu State Key Laboratory of Integrated Service Networks Xidian University Xi'an China School of Computing and Artificial Intelligence Southwest Jiaotong University China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology India Guangzhou Institute of Technology Xidian University Xi'an China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Traffic classification is an essential part of intelligent network management in Fifth generation (5G) networks, from quality of service guarantees to security monitoring. With the evolution of diverse services and applications over 5G networks, traffic patterns and their implications also becoming more complicated, especially in wireless networks. How to dynamically manage the growing traffic demand and satisfy the quality of service (QoS) needs of new applications is particularly critical. The biggest challenge is how to identify traffic types accurately without application information and manual features. However, the existing deep learning (DL)-based classifiers heavily rely on extensive and high-quality samples, which proves impractical to obtain in wireless networks. To overcome this limitation, we propose an ensemble Learning based traffic classification model to provide accurate identification with small-scale datasets, where Attention-based Long Short-Term Memory networks are bagged to train this classifier. The ensemble learning approach employs a combinatorial design involving five base models to construct more accurate and robust learning models. This enables effective feature absorption, making it feasible to train classifiers on limited sample sets. Comprehensive experiments are conducted using a real-world dataset covering 16 applications. Results demonstrate that, even with a mere 10% of training samples, our proposed model achieves a classification accuracy of 95.57% and a precision of 95.83%, surpassing multiple cutting-edge methods.

关键词： Training Accuracy 5G mobile communication Wireless networks Computational modeling Quality of service Telecommunication traffic

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Experimental Demonstration of ML-Based DWDM System Margin Estimation

arXiv

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arXiv 2023年

作者： Müller, Jasper Slyne, Frank Kaeval, Kaida Troia, Sebastian Fehenberger, Tobias Elbers, Jörg-Peter Kilper, Daniel C. Ruffini, Marco Mas-Machuca, Carmen ADVA Fraunhoferstr. 9a Martinsried Munich82152 Germany Communication Networks Technical University of Munich Munich Germany Connect Research Centre School of Computer Science and Statistics Trinity College Dublin Ireland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan20133 Italy

SNR margins between partially and fully loaded DWDM systems are estimated without detailed knowledge of the network. The ML model, trained on simulation data, achieves accurate predictions on experimental data with an... 详细信息

关键词： Dense wavelength division multiplexing

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Adjacent Context Coordination Network for Salient Object Detection in Optical Remote Sensing Images

arXiv

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arXiv 2022年

作者： Li, Gongyang Liu, Zhi Zeng, Dan Lin, Weisi Ling, Haibin Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai200444 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Computer Science Stony Brook University Stony BrookNY11794 United States

Salient object detection (SOD) in optical remote sensing images (RSIs), or RSI-SOD, is an emerging topic in understanding optical RSIs. However, due to the difference between optical RSIs and natural scene images (NSIs), directly applying NSI-SOD methods to optical RSIs fails to achieve satisfactory results. In this paper, we propose a novel Adjacent Context Coordination Network (ACCoNet) to explore the coordination of adjacent features in an encoder-decoder architecture for RSI-SOD. Specifically, ACCoNet consists of three parts: an encoder, Adjacent Context Coordination Modules (ACCoMs), and a decoder. As the key component of ACCoNet, ACCoM activates the salient regions of output features of the encoder and transmits them to the decoder. ACCoM contains a local branch and two adjacent branches to coordinate the multilevel features simultaneously. The local branch highlights the salient regions in an adaptive way, while the adjacent branches introduce global information of adjacent levels to enhance salient regions. Additionally, to extend the capabilities of the classic decoder block (i.e., several cascaded convolutional layers), we extend it with two bifurcations and propose a Bifurcation-Aggregation Block to capture the contextual information in the decoder. Extensive experiments on two benchmark datasets demonstrate that the proposed ACCoNet outperforms 22 state-of-the-art methods under nine evaluation metrics, and runs up to 81 fps on a single NVIDIA Titan X GPU. The code and results of our method are available at https://***/MathLee/ACCoNet. © 2022, CC BY-NC-ND.

关键词： Signal encoding

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Statistical methods and models based on quality of experience distributions

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Quality and User Experience 2020年第1期6卷 1-27页

作者： Seufert, Michael Chair of Communication Networks Insitute of Computer Science University of Würzburg Würzburg Germany

Due to biased assumptions on the underlying ordinal rating scale in subjective Quality of Experience (QoE) studies, Mean Opinion Score (MOS)-based evaluations provide results, which are hard to interpret and can be misleading. This paper proposes to consider the full QoE distribution for evaluating, reporting, and modeling QoE results instead of relying on MOS-based metrics derived from results based on ordinal rating scales. The QoE distribution can be represented in a concise way by using the parameters of a multinomial distribution without losing any information about the underlying QoE ratings, and even keeps backward compatibility with previous, biased MOS-based results. Considering QoE results as a realization of a multinomial distribution allows to rely on a well-established theoretical background, which enables meaningful evaluations also for ordinal rating scales. Moreover, QoE models based on QoE distributions keep detailed information from the results of a QoE study of a technical system, and thus, give an unprecedented richness of insights into the end users’ experience with the technical system. In this work, existing and novel statistical methods for QoE distributions are summarized and exemplary evaluations are outlined. Furthermore, using the novel concept of quality steps, simulative and analytical QoE models based on QoE distributions are presented and showcased. The goal is to demonstrate the fundamental advantages of considering QoE distributions over MOS-based evaluations if the underlying rating data is ordinal in nature.

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