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SSRN 2024年

作者： Wang, Junfen Liu, Peiyue Zhao, Zhanmin Shi, Xuemei Li, Mingliang Wang, Han Li, Ting School of Information Engineering Hebei GEO University Hebei Shijiazhuang050031 China Intelligent Sensor Network Engineering Research Center of Hebei Province Hebei GEO University Hebei Shijiazhuang050031 China Department of Electromechanical Engineering Shijiazhuang College of Applied Technology Hebei Shijiazhuang050081 China Institute of Biomedical Engineering Chinese Academy of Medical Sciences Peking Union Medical College Tianjin300192 China

Tunable diode laser absorption spectroscopy (TDLAS) has been widely used in breath diagnosis and measurement of end-tidal carbon dioxide. The measurement accuracy is severely affected by noise such as background noise and optical fringes. The absorption spectroscopy and second harmonic signal of carbon dioxide (CO2) at 1572.66 nm were simulated. Gaussian noise and sinusoidal periodic signal were added to imitate noise in experiments. For improving signal-to-noise ratio (SNR), an adaptive threshold empirical mode decomposition (EMD) denoising method based on enhanced artificial jellyfish search (EAJS) algorithm was proposed. The relationship between threshold and SNR was established. The optimal threshold for each order of noise dominated intrinsic mode function (IMF) and mixed IMF were obtained by EAJS algorithm. The noise dominated IMF and mixed IMF were processed by soft-thresholding based on the optimal threshold, and the signal denoised was reconstructed. Compared with traditional EMD, EMD soft threshold and wavelet soft threshold, EMD-EAJS algorithm had minimum root-mean-square error (RMSE) of 0.7729%, maximum coefficient of determination (R2) of 99.9640%, SNR of 34.5818 dB and Pearson correlation coefficient (P) of 99.9825%. The simulation results indicated that the proposed method could effectively reduce the measurement error. © 2024, The Authors. All rights reserved.

关键词： Empirical mode decomposition

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Deep Reinforcement Learning Based Multistage Profit Aware Task Scheduling Algorithm for Computing Power network

Deep Reinforcement Learning Based Multistage Profit Aware Ta...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xiaoyao Huang Remington R. Liu Bo Lei Guanglun Huang Baoxian Zhang Network Technology Institute Research Institute China Telecom China School of Information and Communication Engineering Beijing University Of Posts and Telecommunications China School of Computer Science and Information Security Guilin University of Electronic Technology China Research Center of Ubiquitous Sensor Networks University of Chinese Academy of Sciences China

Computing power network (CPN), which integrates heterogeneous computing resources and communication network, can tackle the challenges brought by the pervasiveness of mobile and Internet of Things applications. In this paper, we study the optimization of task scheduling in a CPN network by considering the unbalancing between task distribution and resource cost. The design objective is to maximize the system profit while satisfying tasks' delay requirements. We formulate this problem as an integer programming problem. To address this NP-hard problem, we propose a Deep Reinforcement Learning (DRL) based multistage profit-aware task scheduling algorithm which first makes coarse grained task allocation using DRL among regions and then determines an optimized intra-region task assignment by using profit-aware balancing algorithm. Extensive simulations are conducted for performance evaluation and the results show the high performance of the proposed algorithm as compared with baseline algorithms.

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Manipulation of Valley-Pseudospin in 2d Wte2/CrI3 Van Der Waals Heterostructure by Magnetic Proximity Effect

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SSRN 2023年

作者： Zhang, Wenli Zhu, Haoran Zhang, Wenqi Wang, Jing Zhang, Tiantian Yang, Shiru Shao, Bin Zuo, Xu College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Nankai University Tianjin300350 China Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin Tianjin300350 China Engineering Research Center of Thin Film Optoelectronics Technology Ministry of Education Tianjin300350 China

Lifting the valley degeneracy is an essential condition for exploiting the valley degrees of freedom to process and store information in valleytronics. The magnetic proximity effect (MPE) has been proven to be an effective method for introducing a magnetic field via interlayer exchange interaction, which can be conveniently achieved by two-dimensional (2D) van der Waals (vdW) heterostructure engineering. We have investigated the electronic properties and valley physics of 2D WTe2/CrI3 heterostructure using first-principles calculations. The results show that a significant valley splitting of 34.01 meV can be obtained in the WTe2/CrI3 heterostructure due to the simultaneous breaking of inversion and time-reversal symmetry, which sensitively depends on the atomic overlap of the projected position in the specific stacking model. In addition, we also show that the valley splitting can be enhanced by applying a vertical external electric field (E-field) or reducing the interlayer distance. More than that, it can be boosted by an order of magnitude (~ 115.25 meV) through Cr ion intercalation. This is attributed to that the self-intercalated Cr ion has ferromagnetic ordering and perpendicular magnetic anisotropy. This result provides fundamental insights and valuable guidance for the application of 2D WTe2/CrI3 heterostructure in novel spintronic and valleytronic devices. © 2023, The Authors. All rights reserved.

关键词： Degrees of freedom (mechanics)

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Wide-bandwidth mode converter based on a dual-hollow-core anti-resonant fiber

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Results in Physics 2024年 59卷

作者： Hou, Chenyang Ding, Jinmin Zhao, Yiming Liu, Hongwei Hu, Zixia Meng, Fanchao Liang, Sheng Key Laboratory of Education Ministry on Luminescence and Optical Information Technology National Physical Experiment Teaching Demonstration Center School of Physical Science and Engineering Beijing Jiaotong University Beijing 100044 China Jeme Tienyow Honors College Beijing Jiaotong University Beijing 100044 China Intelligent Sensor Network Engineering Research Center of Hebei Province Information Engineering College Hebei GEO University Shijiazhuang 050031 China

This paper proposes a broadband mode converter utilizing a dual-hollow-core anti-resonant fiber (DHC-ARF) for the first time, in which the mode index matching between LP01 and LP11 modes is achieved by adjusting the c...

This paper proposes a broadband mode converter utilizing a dual-hollow-core anti-resonant fiber (DHC-ARF) for the first time, in which the mode index matching between LP₀₁ and LP₁₁ modes is achieved by adjusting the core diameter size of the hollow-core anti-resonant fiber (HC-ARF). Notably, the air gap can become the main channel for the energy exchange between the two cores by adjusting the air gap size between the two cores of the DHC-ARF. The coupling length of the mode converter is greatly reduced, and a compact and broadband mode converter is realized. Simulation results demonstrate that the proposed mode converter achieves a coupling efficiency (CE) of 92.79 % at a wavelength of 1.55 μm at a length of 10.7 mm. The bandwidth of 330 nm at a CE greater than 90 % covers the E + S + C + L + U bands. The proposed mode converter exhibits a large core diameter compatible with hollow-core fiber communication and is expected to be applied for high-speed and high-capacity hollow-core mode-division-multiplexing fiber-optic communication. The device design methodology presented in this paper can also provide technical references for developing higher-order mode converters, such as LP₀₁ mode to LP₀₂ mode and LP₀₁ mode to LP₂₁ mode, based on DHC-ARF. © 2024

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Distributed Resource Allocation and Computation Offloading Based on Blockchain in Vehicle Edge Computing

Distributed Resource Allocation and Computation Offloading B...

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Future Communications and networks (FCN), International Conference on

作者： Xiaoting Ruan Fei Shen Feng Yan Lianfeng Shen Yi Wu Liang Tang Yu Zhao Zhiyong Bu Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Shanghai Spacecom Satellite Technology Ltd Shanghai China National Mobile Communications Research Laboratory Southeast University Nanjing China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou China

ISBN: (数字)9798331512880

ISBN: (纸本)9798331512897

Mobile edge computing (MEC) extends cloud computing capabilities to the network edge, enabling efficient processing of compute-intensive tasks from resource-constrained devices. However, static edge servers can lead to service vulnerabilities due to dynamic user device locations. This paper introduces a vehicle edge computing (VEC) architecture, leveraging vehicles’ idle computing resources to serve as mobile edge servers for nearby vehicles. As the number of vehicles and compute-intensive tasks increases, substantial data exchanges between vehicles and edge servers raise significant security risks in VEC networks lacking security measures. To address the security challenges and manage computing resources, we integrate VEC with blockchain technology, creating a secure offloading framework. Focusing on video task offloading generated by vehicle applications, we propose a resource allocation scheme that comprehensively considers video compression, offloading decisions, bandwidth, and computational resource constraints. This scheme is formulated as a mixed, discrete, non-convex joint optimization problem aimed at maximizing system revenue. Since this type of problem is usually an NP-hard problem, discrete variable relaxation and product term replacement are applied to transform it into a convex optimization problem, and an alternating direction method of multipliers-based distributed resource allocation (ADMM-DRA) algorithm is proposed to solve it. Simulation experiments show that compared with other algorithms, ADMM-DRA can achieve the highest system revenue, effectively reduce system latency, and improve resource utilization.

关键词： Multi-access edge computing Transforms Video compression Convex functions Blockchains Resource management Servers Security Vehicle dynamics Optimization

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From Universe to Metaverse: IRS-Assisted Efficient Transmission for Hybrid Earth-Moon network

From Universe to Metaverse: IRS-Assisted Efficient Transmiss...

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IEEE Conference on Vehicular technology (VTC)

作者： Chengcheng Lv Fei Shen Feng Yan Lianfeng Shen Yi Wu Liang Tang Yu Zhao Zhiyong Bu Key Laboratory of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China National Mobile Communications Research Laboratory Southeast University Nanjing China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou China Shanghai Spacecom Satellite Technology Ltd. Shanghai China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

To fulfill the requirements of human lunar exploration programs and establish bases on the moon in the distant future, lunar sensors (LSs) will inevitably produce a significant amount of data. It is necessary to construct the Earth-Moon metaverse in order to obtain and utilize lunar information more effectively. Due to the long communication distance between Earth and Moon as well as the lack of communication resources, a hybrid Earth-Moon metaverse network with channel model and transmission model is designed. To ensure the efficiency and stability of transmission in the network, LSs transmit lunar data to Earth clients (ECs) through the active and passive intelligent reflecting surface (IRS) deployed at relay satellites. Then, we propose a Stackelberg game model to describe the adversarial relationship between the satellites, LSs and ECs, and optimal strategies are obtained by solving the Nash equilibrium to maximize their utility. Simulations demonstrate that the network can effectively shorten the transmission delay and improve the utility of ECs and satellites.

关键词： Earth Vehicular and wireless technologies Metaverse Moon Games Nash equilibrium Vectors Delays Channel models Optimization

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Analysis of Satellite Interference Source Location Algorithm

Analysis of Satellite Interference Source Location Algorithm

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International Conference on Computer and Communications (ICCC)

作者： Hui Yan Laiding Zhao Gengxin Zhang Cen Ruan Key Laboratory of Broadband Wireless Communication and Sensor Network Technology "Telecommunication and Network" National Engineering Research Center Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665409513

There are many methods and principles of satellite interference source location, but no matter which method is chosen, the positioning equation is nonlinear. Therefore, it is very important to solve the nonlinear equations accurately for the accuracy of the final positioning results. In this paper, an improved particle swarm optimization (PSO) algorithm is proposed. Based on the combination of PSO and grid search algorithm, the precision requirement is set in the iterative stop condition. When the calculation results meet the set precision, iteration can be stopped. Compared with the ordinary particle swarm optimization algorithm, the results can be obtained more quickly and accurately.

关键词： Satellites Nonlinear equations Military computing Interference Position measurement Iterative algorithms Mathematical models

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Joint DBF and SAO parallel filtering based on multithread load balancing

Joint DBF and SAO parallel filtering based on multithread lo...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Ma, Hao Hu, Dong Li, Yi Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

This paper presents a joint parallel loop filtering algorithm based on multi-thread load balancing in HEVC decoding, which implements the parallel processing of deblocking filtering (DBF) and sample adaptive compensation (SAO). Because of the diversity of video, the texture of different regions in an image is also different, which leads to various CTU partition methods. Therefore, the number of the boundary to be filtered is greatly different, resulting the computation load among multiple threads unbalanced in parallel processing. To solve this problem, an area division scheme is proposed, which divides the image into multiple areas, and the number of boundaries to be filtered in each area is similar. Then, the mapping relationship table is used to allocate these areas to multiple threads for parallel processing, so as to achieve the load balancing among the filtering threads. Finally, the cache technology is used to combine DBF and SAO to reduce the delay between them and improve the overall parallelism of the loop filter. Experimental results show that the performance of the proposed load balancing joint filtering algorithm is 8.15% higher than the previous scheme. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Textures

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Scale adaptive block target tracking based on multi-layer convolution features and Kernel correlation filter

Scale adaptive block target tracking based on multi-layer co...

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2020 International Symposium on Automation, Information and Computing, ISAIC 2020

作者： Zhang, Ting Hu, Dong Zhang, Jing Jiangsu Province’s Key Lab of Image Procession and Image Communications Nanjing210003 China Education Ministry’s Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing210003 China Education Ministry’s Engineering Research Center of Ubiquitous Network and Heath Service Nanjing University of Posts and Telecommunications Nanjing210003 China

Target tracking is currently a hot research topic in Computer Vision and has a wide range of use in many research fields. However, due to factors such as occlusion, fast motion, blur and scale variation, tracking method still needs to be deeply studied. In this paper, we propose a block target tracking method based on multi-convolutional layer features and Kernel correlation filter. Our method divides the tracking process into two parts: target position estimation and target scale estimation. First, we block the target frame based on the condition number. Second, we extract the features by the convolutional layer and apply it to the kernel correlation filter to get the center position of different block targets. With the reliability of different blocks measured by the Barker coefficient, the overall target position center is obtained. Then, the affine transformation is adopted to achieve the scale adaptation. The algorithm in this paper is evaluated by the public video sequences in OTB-2013. Numerous experimental results demonstrate that the proposed tracking method can achieve target scale adaptation and effectively improve the tracking accuracy. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Target tracking

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Tme: Temporal and Motion Enhancement for Action Recognition

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SSRN 2022年

作者： Wang, Hao Liu, Guohua Zhao, Bin Sensor Technology Intelligent System Lab College of Electronic Information and Optical Engineering Nankai University Tianjin300350 China School of Artificial Intelligence Guilin University of Electronic Technology Guangxi Guilin541004 China Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology Tianjin300350 China Engineering Research Center of Thin Film Optoelectronics Technology Ministry of Education Nankai University Tianjin300350 China

The key to video understanding is to capture temporal information. 3D CNNs can achieve good performance by directly fusing spatial and temporal features but are computationally intensive. Conventional 2D CNNs perform well in image recognition, but their inability to extract temporal features leads to poor performance in video action recognition. In this work, we aim to identify temporal relationships for video action recognition. To this end, we propose a temporal and motion enhancement (TME) module consisting of two submodules: multipath temporal enhancement(MTE) module and a long short-range Motion Enhancement(LSME) module. The MTE module consists of four paths: the residual path, temporal aggregation(TA) path, channel-wise temporal excitation(CTE) path and spatial-attention temporal excitation(STE) path. The residual path makes part of the information in the original activation accessible. The TA path employs convolution on the temporal dimension to characterize temporal representation. The CTE path rebuilds channelwise feature relationships in terms of the temporal aspect. The STE path introduces attention mechanisms and explores the temporal modeling. We aggregate features of different paths by splitting channels, which is efficient for model inference. The LSME module calculates feature-level temporal differences between long and short range frames, which enhances the motion-related features. The TME module is plug-and-play and can be inserted into 2D CNNs to form an effective TMENet with limited extra computational cost. The extensive experiments on several action recognition datasets, such as Kinetics400, Something-Something V1&V2, HMDB51, and UCF101, demonstrate that the TMENet achieves the state-of-the-art performance with high efficiency. The code will be released soon. © 2022, The Authors. All rights reserved.

关键词： Image recognition

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