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IEEE Conference on Global Communications (GLOBECOM)

作者： Shupei Zhang Boya Di Hongliang Zhang H. Vincent Poor State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering Princeton University USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Reconfigurable holographic surfaces (RHSs) have been proposed as a cost-effective and power-efficient solution for extremely large-scale arrays, where the amplitude of electromagnetic waves radiated at each element is controlled to achieve high directive gain. However, the complexity of acquiring real-time channel state information (CSI) required for beamforming is prohibitively high, especially when the near-field expansion brought by the large-scale RHS is considered. In this paper, we propose a codebook-based beam training scheme for a large-scale RHS-enabled communication system to bypass CSI estimation. Unlike traditional phase-controlled arrays, the amplitude-controlled property of the RHS implies that each RHS element can be selectively activated. This motivates an array reconfiguration method where a scale-changeable RHS array is constructed to generate gain-flat beams with different coverage in the angle-range domain. A hierarchical RHS codebook is then proposed where the coverage of the codewords in each layer is progressively refined. To address the substantial beam search overhead in the near-far field, a two-stage beam training scheme is performed in the proposed codebook, thereby reducing the overhead to a logarithmic level of the element number. The simulation results show that the proposed scheme performs better than phased arrays given the same input power in terms of sum rate, and it also approaches the upper bound achieved by the exhaustive search at a significantly reduced overhead.

关键词： Training Phased arrays Upper bound Surface waves Simulation Electromagnetic scattering Estimation Real-time systems Complexity theory Global communication

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Mission-Driven Resource Scheduling in Satellite-Terrestrial networks: From Perspective of Collaboration and Reconfiguration

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IEEE Transactions on Communications 2025年

作者： Zhou, Di Sheng, Min Bao, Chenxi Wang, Yixin Li, Jiandong Han, Zhu Xidian University State Key Laboratory of Integrated Service Networks Shaanxi Xi'an710071 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Satellite-terrestrial networks (STNs) are emerging as a promising solution for provisioning comprehensive services, such as the Internet of Remote Things (IoRT) and remote sensing, within the realm of 6G wireless networks. Nonetheless, resource failures and the exigencies of diverse mission urgencies exacerbate the intricacies of resource scheduling in STNs, thus impeding the effective alignment of distinct mission requirements with dynamic resources. In light of these challenges, we first mathematically formulate the complex resource scheduling problem in STNs as a stochastic optimization paradigm, endeavoring to maximize the number of successfully accomplished missions. Subsequently, we conceptualize the resource evolution to delineate scheduling dynamics, encompassing potential contingencies of resource discontinuities. Next, we propose an innovative hierarchical deep learning-based mission-driven resource scheduling (HDL-MDRS) algorithm, aimed at optimizing resource collaboration and reconfiguration to amplify network performance within the dynamic ambits characterized by resource disruptions. The HDL-MDRS algorithm achieves a coarse-grained alignment of diverse mission requirements with multidimensional resources. It enhances overall mission fulfillment and network resource utilization efficiency through fine-grained collaboration and reconfiguration among satellites, both within and across different clusters. Notably, the simulation findings substantiate the effectiveness of the HDL-MDRS algorithm, effectively ensuring the requirements of different types of missions in case of the unforeseen resource failures, orchestrated through efficient resource collaboration and on-demand reconfiguration. © 2025 IEEE.

关键词： Stochastic systems

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hao, Fengqi Zhu, Shulei Ma, Dexin Dong, Xiangjun Kong, Hoiio Liu, Xia Mu, Chunhua Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China City University of Macau Faculty of Data Science 999078 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China Communication College Qingdao Agricultural University Qingdao266109 China Maize Research Institute Shandong Academy of Agricultural Sciences National Engineering Laboratory of Wheat and Maize Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Jinan250100 China

ISBN: (纸本)9781665410205

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction - receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings. © 2024 IEEE.

关键词： Deep learning Maize seedling detection Object detection UAV

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Deep belief network-hidden Markov model based nonlinear equalizer for VCSEL based optical interconnect

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Science China(Information Sciences) 2020年第6期63卷 155-163页

作者： Fukui TIAN Chuanchuan YANG State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University

The data center has developed rapidly over the past few years, leading to the demand for high speed data transmission. Vertical cavity surface emitting lasers(VCSELs) based optical interconnect is evolving to 100 Gb/s and beyond, which makes nonlinear distortions difficult to be compensated or equalized by conventional equalizers. Moreover, the challenge becomes very complicated for conventional equalizers because of the presence of inter-symbol interference(ISI) together with the nonlinear distortions. So many neural network based DSP algorithms such as artificial neural network(ANN) have been proposed to mitigate the distortions. However, ANN has the limitations that sample's relevant information is not considered,leading to degradation in ANN's performance and high computational complexity. In this paper, in order to maintain an excellent capability of mitigating nonlinear distortions like other neural network based equalizers while considering the sample's relevant information to reduce the computational complexity, we propose a deep belief network-hidden Markov model(DBN-HMM) based nonlinear equalizer which is tested in a PAM-4 modulated VCSEL and multimode fiber(MMF) optical interconnect link experimentally. The BER performance can be greatly improved compared with conventional DSP algorithms. In addition, the computational complexity of DBN-HMM based equalizer can be about 41% lower than that of ANN based method with a similar BER performance.

关键词： short-range optical interconnect vertical cavity surface emitting laser(VCSEL) nonlinear equalizer deep belief network(DBN) hidden Markov model(HMM)

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Transmission Energy Allocation for Over-the-Air Computation with Energy Harvesting

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Journal of Communications and Information networks 2024年第2期9卷 126-136页

作者： Siyao Zhang Zixiang Ren Xinmin Li Yin Long Jie Xu Shuguang Cui School of Computer Science and Technology Southwest University of Science and TechnologyMianyang 621010China Shenzhen Future Network of Intelligence Institute(FNii-Shenzhen) The Chinese University of Hong KongShenzhenGuangdong 518172China School of Information Science and Technology University of Science and Technology of ChinaHefei 230027China FNii-Shenzhen The Chinese University of Hong KongShenzhenGuangdong 518172China College of Computer Science Chengdu UniversityChengdu 610100China School of Science and Engineering FNii-Shenzhenand Guangdong Provincial Key Laboratory of Future Networks of IntelligenceThe Chinese University of Hong KongShenzhenGuangdong 518172China

Over-the-air computation(AirComp)has recently emerged as a promising multiple-access technique for fast wireless data aggregation(WDA)from distributed wireless devices(WDs). This paper investigates an energy harvesting (EH) AirComp system, in which multiple EH-powered single-antenna WDs simultaneously send wireless signals to a single-antenna access point (AP) with conventional energy supply for WDA via AirComp. Under this setup, we minimize the average computation mean square error(MSE)over a particular time period, by jointly optimizing the transmit energy allocation at the WDs and the AirComp denoising factors at the AP over time, subject to the energy causality constraints at individual WDs. First, we consider the offline scenario by assuming that the energy state information(ESI)and channel state information (CSI) are non-causally known at the beginning of the period, in which the formulated average MSE minimization corresponds to a non-convex optimization problem. We present a high-quality converged solution by using the techniques of alternating optimization and convex optimization. It is shown that for each WD,if the EH rate is sufficiently high,then the channel inversion power allocation is adopted;while if the EH rate is low, then all the harvested energy should be used up for transmission with proper energy allocation over time. Next, we consider the online scenario with causal ESI and CSI,in which the MSE minimization becomes a stochastic optimization *** this scenario, we present an offline-inspired online algorithm to obtain efficient online energy allocation designs by utilizing the obtained offline solutions. Finally,numerical results show that the proposed designs significantly outperform two benchmark schemes with power-halving and full-power transmission,respectively.

关键词： over-the-air computation(AirComp) power allocation energy harvesting(EH) optimization

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Tensor Based Sparse Estimation for RIS-Aided Massive MIMO 23

Tensor Based Sparse Estimation for RIS-Aided Massive MIMO

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Li, Weilai Wei, Li Huang, Chongwen Yang, Jun Yuen, Chau Zhang, Zhaoyang School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zte Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China

ISBN: (纸本)9798350325959

Reconfigurable intelligent surface (RIS) is intended as a potential technique for future 6-th Generation wireless communication system. However, acquiring accurate channel state information is challenging since the channel matrices are high-dimensional due to large number of reflective elements (REs) in RIS. In this paper, we investigate the problem of sparse channel estimation (CE) in massive multiple-input multiple-output (MIMO) system equipped with large RIS (IRIS), where there are only few REs that are active during CE phase. We propose a two-stage tensor based CE algorithm that exploits the merits of IRIS. Specifically, IRIS act as reflector in the first stage of CE. Then, we identify the condition for the IRIS to activate few specified REs in the second stage. Simulation results demonstrate that the proposed algorithm outperforms existing CE schemes. © 2023 IEEE.

关键词： massive MIMO RIS Sparse channel estimation tensor decomposition

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LIMO: Load-balanced Offloading with MAPE and Particle Swarm Optimization in Mobile Fog networks

LIMO: Load-balanced Offloading with MAPE and Particle Swarm ...

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

作者： Yasaman Seraj Soheil Fadaei Bardia Safaei Ali Javadi Amir Mahdi Hosseini Monazzah Ali Mohammad Afshin Hemmatyar Reliable And Durable IoT Applications & Networks (RADIAN) Research Laboratory Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Iran University of Science and Technology (IUST) Tehran Iran

ISBN: (数字)9798331529284

ISBN: (纸本)9798331529291

Fog computing is essentially the expansion of cloud computing towards the network edge, reducing user access time to computing resources and services. Various advantages attribute to fog computing, including reduced latency, and improved user experience. However, user mobility may limit the benefits of fog computing. The displacement of users from one location to another, may increase their distance from a fog server, leading into latency amplification. This would also increase the probability of over utilization of fog servers which are located in popular destinations of mobile edge devices. This creates an unbalanced network of fog devices failing to provide lower makespan and fewer cloud accesses. One solution to maintain latency within an acceptable range is the migration of fog tasks and preserve the distance between the edge devices and the available resources. Although some studies have focused on fog task migration, none of them have considered load balancing in fog nodes. Accordingly, this paper introduces LIMO; an allocation and migration strategy for establishing load balancing in fog networks based on the control loop MAPE (Monitor-Analyze-Plan-Execute) and the Particle Swarm Optimization (PSO) algorithm. The periodical migration of tasks for load balancing aims to enhance the system’s efficiency. The performance of LIMO has been modeled and evaluated using the Mobfogsim toolkit. The results show that this technique outperforms the state-of-the-art in terms of network resource utilization with 10% improvement. Furthermore, LIMO reduces the task migration to cloud by more than 15%, while it reduces the request response time by 18%.

关键词： Cloud computing Load management User experience Servers Resource management Time factors Particle swarm optimization Monitoring Edge computing Load modeling

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

ISBN: (纸本)9781957171395

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Optical fibers

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Machine Learning for Caching Placement in Edge Computing networks

Machine Learning for Caching Placement in Edge Computing Net...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Liang Zhang Bijan Jabbari Department of Electrical and Computer Engineering George Mason University Fairfax VA USA Department of Electrical and Computing Engineering Communications and Networks Laboratory George Mason University Fairfax VA USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Internet of Things devices (IoTDs) that deployed for various applications have limited size, power, storage, and computing capabilities, and many of them are used for low-latency applications. Mobile edge computing (MEC) can be leveraged to reduce the latency of IoTDs, and judicious caching placement can help further to improve the quality of service. In this paper, we study multi-content placement (MCP) in the MEC networks for IoTDs by considering the background data caching in the edge nodes and the data collection of IoTDs. The MCP problem is formulated by considering the caching, the IoTD assignment, and the communication and computing resources allocation to minimize the average latency of all IoTDs. As the MCP problem is NP-hard, we propose a deep reinforcement machine learning algorithm to obtain the caching placement and IoTD assignment jointly to solve the MCP problem. We use an optimal joint resource-scheduling algorithm to assign the resources. Our results demonstrate that considerable latency improvement can be achieved through the proposed deep reinforcement machine learning algorithm as compared to baseline algorithms.

关键词： Machine learning algorithms Multi-access edge computing Scheduling algorithms Simulation Quality of service Data collection Deep reinforcement learning

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