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Experimental demonstration of 220-GHz terahertz signals wireless transmission over 4.6 km

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Science China(information Sciences) 2025年第1期68卷 386-388页

作者： Yi WEI Jianjun YU Mingxu WANG Xiongwei YANG Qiutong ZHANG Jingwen TAN Bing ZHANG Ying WU Yang HAN Peng TIAN Wen ZHOU Kaihui WANG Feng ZHAO State Key Laboratory of ASIC and System Key Laboratory for Information Science of Electromagnetic Waves (MoE)School of Information Science and Technology Fudan University school of Electronic engineering Xi'an University of Posts and Telecommunications

The advancement of terahertz (THz) communication technology drives the evolution of wireless communication systems,offering novel pathways and technical means for the development of future 6G communication *** wireless communication systems are often constrained by bandwidth limitations of electronic devices in high frequency ***,THz communication technology leverages the characteristics of electromagnetic waves to transcend these limitations,enabling communication athigher frequencies and wider bandwidths.

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Aligning enhanced feature representation for generalized zero-shot learning

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Science China(information Sciences) 2025年

作者： Zhiyu FANG Xiaobin ZHU Chun YANG Hongyang ZHOU Jingyan QIN Xucheng YIN school of Computer&communication engineering University of Science and Technology Beijing

Abstract Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders （VAEs） is a popular strategy for generalized zero-shot learning （GZSL）.However,due to the lack of fine-grained instance-wise annotations,existing VAE methods can easily suffer from the posterior collapse *** this paper,we propose an innovative asymmetric VAE network by aligning enhanced feature representation （AEFR） for *** from general VAE structures,we designed two asymmetric encoders for visual and semantic observations and one decoder for visual ***,we propose a simple yet effective gated attention mechanism （GAM） in the visual encoder for enhancing the information interaction between observations and latent variables,alleviating the possible posterior collapse problem *** addition,we propose a novel distributional decoupling-based contrastive learning （D2-CL） to guide learning classification-relevant information while aligning the representations at the taxonomy level in the latent representation *** experiments on publicly available datasets demonstrate the state-of-the-art performance of our *** source code is available at https://***/seeyourmind/AEFR.

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Low-complexity channel estimation for wideband mmWave massive MIMO systems with uniform planar arrays

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The Journal of China Universities of Posts and Telecommunications 2025年

作者： Wang Haotian Liu Lizhou Qiu Ling Zhang Lei school of Electronic and information engineering， University of Science and Technology of China

Millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) are broadly recognized as key enabling technologies for the fifth generation communication systems. In this paper， a low-complexity angle-delay parameters estimation (ADPE) method was put forward for wideband mmWave systems with uniformly planar arrays (UPA). In particular， ADPE method effectively decouples the angle-delay parameters and converts the angle-delay estimation problem into three independent subproblems. Accordingly， the ability to devise an off-grid method based on discrete Fourier transform (DFT) with a closed-form solution for angle-delay estimation and potential path number acquisition can be realized. In actuality， only a limited number of potential paths are close to the true paths influenced by noise. Consequently， the removal of noise paths to acquire the corresponding true path gains through a sparsity adaptive path gains estimation (APGE) method is postulated. Finally， the simulation results substantiate the effectiveness of ADPE and APGE methods.

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Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho school of Computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of AI and Software engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the school of Biomedical engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

PltcRB: a practical distributed randomness beacon with optimal amortized communication complexity

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Science China(information Sciences) 2025年

作者： Zheyi WU Haolin LIU Lei WANG school of Electronic information and Electrical engineering Shanghai Jiao Tong University

The distributed randomness beacon (DRB) is a crucial tool for continuously generating unpredictable, bias-resistant,and publicly verifiable random numbers on a regular basis. This is particularly useful for applications such as lotteries, electronic voting, and cryptographic parameter generation. However, existing studies either require complex communication or rely on a public bulletin board to meet security requirements. This brings a performance bottleneck when dealing with a large number of participants. This paper introduces a novel DRB protocol PltcRB based on timed commitments, eliminating the need for a public bulletin board. Our approach achieves optimal communication complexity of O(n) while maintaining the desired properties of a DRB protocol. The computation complexity is also O(n) when n is larger than the security parameterκ. These results demonstrate the practicality and performance of PltcRB, supported by our experimental analysis under various configurations.

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Deep learning-based software engineering: progress,challenges, and opportunities

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Science China(information Sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG school of Journalism and communication Sun Yat-sen University school of Software Technology Zhejiang University school of Software engineering Sun Yat-sen University school of Software Dalian University of Technology school of Computer Science and Technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software Technology Nanjing University school of Computer Science and engineering Beihang University Institute of information engineering Chinese Academy of Sciences school of Computer Science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University school of Computer and information Technology Beijing Jiaotong University school of Computer Science and Technology Harbin Institute of Technology school of Computer Science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey

Optimization of CZTSSe thin film solar cell performance using PbS back surface field layer

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Optoelectronics Letters 2025年

作者： WENJING LUO ZHIJIAN BAI YUMING XUE LUOXIN WANG YANHONG DING HONGLI DAI Tianjin Key Laboratory of Film Electronic & communication Devices， school of Electronics information engineering， Tianjin University of Technology school of Materials Science and engineering ，Jilin University

CZTSSe is an Earth-abundant， environmentally friendly， and low-cost material， with a current record power conversion efficiency of 14.9%， This result is well below the prediction of the Shockley-Queisser limit. To improve the performance of solar cells， a layer of PbS (BSF) was added to the CZTSSe， which was simulated using the SCAPS-1D software. The optimal photovoltaic performance of AZO/ZnO/CdS/CZTSSe/PbS/Mo was studied by changing the parameters of the CZTSSe absorption layer and PbS layer. Studies have shown that the efficiency can be increased to 20.86% after using PbS.

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The optimal control theory——a scientific approach to fundamentally solving control problems

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Science China(information Sciences) 2025年第1期68卷 389-391页

作者： Huanshui ZHANG school of Electrical and Automation engineering Shandong University of Science and Technology school of Control Science and engineering Shandong University

The maximum principle has bridged mathematical optimization to optimal control,ushering in significant developments and refinements in optimal control theory,notably during the 1960s with the advent of linear quadratic (LQ)control and linear quadratic estimation (LQE).This progression propelled optimal control theory into further advancements,encompassing stochastic control,robust/H-infinity control,model predictive control (MPC),networked control,and reinforcement learning *** control,established upon a rigorous mathematical foundation,extends static optimization theory to dynamic systems,exhibiting scientific essence,unity,and ***,since its inception,optimal control theory has served as an indispensable core role across all control-related domains,including communication-constrained control in networked systems,consensus control,cooperative control,and reinforcement learning control.

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BER performance analysis of non-Hermitian symmetry OFDM-VLC systems with ADC quantization noise

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Optoelectronics Letters 2025年

作者： Wang Zhongpeng Ai Caihua Zhang Lijuan school of information and Electronic engineering， Zhejiang University of Science and Technology

Quantization noise caused by analog-to-digital converter (ADC) gives rise to the reliability performance degradation of communication systems. In this letter， a quantized non-hermitian Symmetry (NHS) orthogonal frequency-division multiplexing-based visible light communication (OFDM-VLC) system is presented. In order to analyze the effect of the resolution of ADC on NHS OFDM-VLC， a quantized mathematical model of NHS OFDM-VLC is established. Based on the proposed quantized model， a closed-form bit rate error (BER) Expression is derived. The theoretical analysis and simulation results both confirm the effectiveness of the obtained BER formula in high-resolution ADC. In addition， channel coding is helpful in compensating for the BER performance loss due to the utilization of lower resolution ADC.

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