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Resource allocation optimization for SWIPT full-duplex relaying networks with practical energy harvester

The Journal of China Universities of Posts and Telecommunications 2023年第1期30卷 39-46页

作者： Xu Siyang Song Xin Zhu Jiahui School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdao 066004China

In this paper,the resource allocation optimization for the simultaneous wireless information and power transfer(SWIPT)full-duplex(FD)relaying networks is investigated,in which the power-constrained relay scavenges energy from the source signal and assists information transmission by FD *** into account non-linear energy harvesting(EH)hardware circuit characteristics of the relay,the information rate maximization problem is developed by jointly optimizing the time-switching(TS)factor and transmission powers of the source in two different ***,the formulated optimization is highly non-convex and difficult to *** cope with this problem,the primary problem is decomposed into two sub-problems with respect to the TS factor and transmission *** solving these two sub-problems,the final sub-optimal solutions can be obtained by alternating *** results prove that the optimization of both TS factor and transmission powers can effectively enhance the information rate for the considered networks.

关键词： resource allocation optimization full-duplex relaying networks energy harvesting information rate

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A Reinforcement Learning Based Approach to Partition Testing

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计算机科学技术学报（英文版） 2025年第1期40卷 99-118页

作者：孙昌爱肖明俊代贺鹏刘淮 School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computing Technologies Swinburne University of Technology Hawthorn Australia

Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these *** by the theory of software cybernetics,some strategies have been proposed to dynamically se-lect partitions based on the feedback information gained during *** basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly ac-cording to the previous test *** a feedback-driven mechanism can be considered as a learning process—it makes decisions based on the observations acquired in the test ***,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition *** this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing *** algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed *** studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 *** experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing *** study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.

关键词： partition testing reinforcement learning temporal-difference learning intelligent software engineering

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Photonic generation of multi-format and reconfigurable microwave signals based on cascaded external modulation

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Optical and Quantum Electronics 2025年第3期57卷 1-14页

作者： Chen, Mo Chen, Hongyao Zhang, HongYu Li, ZhuoYang Wang, Jianping Feng, LiFang Lu, HuiMin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing10083 China

This paper presents a photonic scheme for generating multi-format, multi-band, and reconfigurable microwave photonic signals through cascaded external modulation. The proposed system utilize dual-parallel Mach–Zehnder modulators (DP-MZM) and Mach–Zehnder modulators (MZM) to produce high-order optical sidebands, enabling flexible frequency multiplication factors switching and pulse waveform reconstruction. Additionally, using a polarization control structure allows for spurious suppression and encoded signal loading. The system can generate various signal formats, including rectangular optical frequency combs, frequency-multiplied signals, dual-frequency encoded signals, and multiple pulse waveforms. Simulation results demonstrate the successful generation of 16 GHz frequency-doubled signal and 32 GHz frequency-quadrupled signal, with spurious suppression ratios of 37.9 dB and 44.8 dB, respectively. It can also generate four-line rectangular optical frequency combs with frequency multiplication factors of 2 and 4, achieving flatness values of 0.24 dB and 0.16 dB. Furthermore, the system can generate symmetric triangular pulses and Sinc-Nyquist pulses with adjustable repetition frequencies, as well as encoded signals with good pulse compression performance, having PCR values close to the theoretical value of 13. This work demonstrates a versatile and reconfigurable approach to microwave photonic signal generation, offering promising applications in advanced radar systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Microwave photonics

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Robust subspace clustering via two-way manifold representation

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Multimedia Tools and Applications 2025年第16期84卷 16339-16356页

作者： Ezeora, Nnamdi Johnson Anichebe, Gregory Emeka Nzeh, Royransom Chiemela Uzo, Izuchukwu Uchenna Department of Computer Science University of Nigeria Nsukka Nigeria School of Computer Science and Communication Engineering JiangSu University JiangSu 212013 China

Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Clustering algorithms

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SCGformer: Smart contract vulnerability detection based on control flow graph and transformer

IET Blockchain

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IET Blockchain 2023年第4期3卷 213-221页

作者： Gong, KeXin Song, Xiangmei Wang, Na Wang, Chunyang Zhu, Huijuan School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China

The security of smart contract has always been one of the significant problems in blockchain. As shown in previous studies, vulnerabilities in smart contracts can lead to unpredictable losses. With the rapid growth of the number of smart contracts, more and more data driven detection technologies based on machine learning have been proposed. However, some state-of-the-art approaches mainly rely on the source code of smart contract. These methods are limited by the openness of the source code and the version of the programming language. To address this problem, we propose a novel vulnerability detection method based on transformer by constructing the control flow graph (CFG) of smart contracts operation codes (opcodes), which shields the difference of various versions of program language. Extensive experiments are conducted to evaluate the effectiveness of the proposed method on the authors' own collected dataset. The experimental results show that the proposed method achieves 94.36% accuracy in vulnerability detection, which performs better than other state-of-the-art methods. © 2023 The Authors. IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Smart contract

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Joint Resource Allocation and Trajectory Optimization Algorithm in UAV-Assisted MEC System Based on DDPG

Joint Resource Allocation and Trajectory Optimization Algori...

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2024 International Annual Conference on Complex Systems and Intelligent Science, CSIS-IAC 2024

作者： Song, Xin Li, Ruomeng Xu, Siyang Zhou, Zijian School of Computer and Communication Engineering Northeastern University at Qinhuangdao Qinhuangdao China School of Computer and Communication Engineering Northeastern University Shenyang China

ISBN: (纸本)9798331504755

Due to the advantages of maneuverability and the small size of Unmanned Aerial Vehicles (UAVs), mobile edge computing (MEC) servers can be deployed for them to provide computing services. Because the computing resources and energy are limited and the tasks have strict time constraints, tasks are prioritized and their age of information (AoI) is recorded. To solve the issues, we propose a deep deterministic policy gradient (DDPG)-based joint resource allocation and trajectory optimization algorithm in UAV-assisted MEC system. We formulate the resource allocation and trajectory problems as a Markov Decision Process (MDP). Then, we propose a DDPG algorithm to solve the formulated problem and minimize the total energy consumption of the system. Simulation results show that the proposed DDPG-based joint optimization algorithm significantly reduces the energy consumption of the system and has a fast convergence rate. © 2024 IEEE.

关键词： Markov processes

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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LGDF-Net: Local and Global Feature Based Dual-Branch Fusion Networks for Deepfake Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5489-5500页

作者： Long, Min Liu, Zhenyu Zhang, Le-Bing Peng, Fei Guangzhou University School of Electronics and Communication Engineering Guangdong Guangzhou510006 China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China Huaihua University School of Computer and Artificial Intelligence Huaihua418000 China Guangzhou University School of Artificial Intelligence Guangdong Guangzhou510006 China

With the rapid development of Deepfake technology, social security is facing great challenges. Although numerous Deepfake detection algorithms based on traditional CNN frameworks perform well on specific datasets, they still suffer from overfitting due to an over-reliance on localized artifact information. This limitation leads to degraded detection performance across diverse datasets. To address this issue, this study proposes a dual-branch fusion network called LGDF-Net. LGDF-Net uses a dual-branch structure to process the local artifact features and global texture features generated by Deepfake separately, preserving their unique characteristics. Specifically, the local compression branch utilizes a specially designed local compression module (LCM) that allows the network to focus more accurately on key regions of localized artifacts in Deepfake faces. The global expansion branch enhances the analysis of the global facial context through a global expansion module (GEM), which captures image context information and subtle texture features more comprehensively. Additionally, the proposed multi-scale feature extraction module (MSFE) delves into image features at various scales, enriching the extraction of detailed information. Finally, the multi-level feature fusion strategy (MLFF) improves the integration of local and global features through multiple layers, enabling the network to learn the intrinsic connections between these two types of features. A series of experimental validations demonstrate that the proposed scheme outperforms many existing detection networks in terms of accuracy and generalization ability. © 1991-2012 IEEE.

关键词： Feature extraction

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Using of metasurface lens and particle swarm optimisation for enhancement of parameters of a pyramidal horn antenna

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Australian Journal of Electrical and Electronics engineering 2023年第1期20卷 85-94页

作者： Abdollahi, Nasrin Mohajeri, Farzad School of Electrical and Computer Engineering Shiraz University/Communication Department Shiraz Iran

In this article, a lens is applied to reduce the horn antenna’s Side Lobe Level (SLL) based on controlling the phase and amplitude distribution of the electromagnetic fields over the aperture of the horn antenna in the X band (8−12 GHz) for radar applications. By locating the metasurface lens inside the standard X band pyramidal horn, the SLL is decreased by 4–15 dB in E-plane and 0.8–7 dB in H-plane. The measured results with metasurface lenses demonstrate the reduction of SLL over the entire bandwidth. Good agreement between measurement and simulation results shows the tapering of field distribution over the aperture of the horn antenna. ©, Engineers Australia.

关键词： Horn antennas

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Optimization Design of Output Combining Network Using Irregular Structure for Broadband DPA

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IEEE Microwave and Wireless Technology Letters 2025年第4期35卷 480-483页

作者： Zhang, Heng Xia, Jing Ni, Zhongpeng Ge, Xiaoshuai Kong, Wa Zhang, Wence Yu, Chao Zhu, Xiao-Wei Jiangsu University School of Computer Science and Communication Engineering Zhenjiang212013 China Southeast University School of Information Science and Engineering Nanjing210096 China

This letter presents an optimization design method for broadband Doherty power amplifier (DPA) using an irregular output combining network (OCN). First, the postmatching network and the output matching networks are considered as the overall OCN, and the three-port S-parameters of the OCN are analyzed and calculated using the impedances at power back-off (PBO) and saturation. Subsequently, to improve the feasibility of optimization, several impedances that meet the requirements are used to calculate feasible S-parameter solution sets, which can be used as the optimization target. Finally, an optimization method of the OCN using an irregular structure is proposed. For verification, a 1.2-2.8-GHz broadband DPA is designed and measured. The results show that with the saturated output power exceeding 43.1 dBm, drain efficiencies of 54.1%-74.2% and 50.9%-56.5% are obtained at saturation and 6-dB PBO, respectively. © 2023 IEEE.

关键词： Doherty amplifiers

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