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

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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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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science China(information sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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FPGA Real-Time Deployment of Low-Complexity Volterra Equalizer Based on Pruning and Non-Uniform Quantization

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Journal of Lightwave technology 2025年第9期43卷 4183-4192页

作者： Sun, Xinda Wang, Kaihui Zhu, Zonghui Zhang, Long Gou, Yumeng Meng, Yuanxiao Chen, Yun Yu, Jianjun Fudan University Department of Communication Science and Engineering State Key Laboratory of ASIC and System Key Laboratory for Information Science of Electromagnetic Waves School of Information Science and Technology Shanghai 200433 China Fudan University Shanghai200433 China

Electronic nonlinear equalization technology is a promising signal impairments compensation technique with great development potential and wide application scenarios. However, its high complexity makes it challenging to implement in field-programmable gate array (FPGA). We propose a low-complexity real-time nonlinear equalization scheme based on FPGA, which can provide similar performance as traditional schemes but only occupies very low digital signal processing (DSP) resources. With the help of this scheme, by using pruning and non-uniform quantization, the computational complexity of a Volterra equalizer with 15 linear taps and 25 nonlinear taps is comparable to that of a Volterra equalizer with 8 linear taps and 4 nonlinear taps. By non-uniformly quantizing the taps of the equalizer, the quantization accuracy can be saved compared to uniform quantization, and after the taps are quantized, complex multiplication can be replaced by simple shift operations, simplifying the complexity of multiplication operations. The scheme of weight pruning and non-uniform quantization significantly reduces the required hardware resources and power consumption. We verify the proposed scheme in a decision-directed Volterra (DD-Volterra) equalizer, The equalizer was successfully deployed in a 230.4 MHz xcvu9pflgb2104-2-I Xilinx FPGA and achieved real-time IM/DD transmission of 29.4912 Gbit/s PAM4 signals over 25 km standard single-mode fiber (SSMF), meeting the 3.8 × 10-3 hard-decision forward error correction (HD-FEC) standard. The results show that the proposed low-complexity Volterra equalizer scheme can reduce DSP resources by up to 75.3% and power consumption by 26.2%. © 1983-2012 IEEE.

关键词： Forward error correction

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Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 215-227页

作者： Ding Wang Lingzhi Hu Xiaoli Li Junfei Qiao IEEE the School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protection and Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator ***, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov *** tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

关键词： Adaptive critic control neural network (NN) nonaffine nonlinear systems online fault-tolerant tracking design uniform ultimate boundedness

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UDI-3DVT: A Novel Network for 3-D Reconstruction via Uncertainty-Driven Sampling and Incremental Visual-Tactile Fusion

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IEEE Sensors Journal 2025年第6期25卷 10358-10369页

作者： Zhang, Chenyang Zhang, Shaohua Li, Yao Chen, Wenbai Gao, Guowei Wu, Peiliang Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Qinhuangdao066004 China Beijing Information Science and Technology University School of Automation Beijing100192 China

The fusion of visual and tactile senses allows robots to reconstruct and understand objects, aiding in downstream tasks such as object recognition and object grasping. However, most existing visual-tactile reconstruction methods typically overlook the role of visual point cloud processing in reconstruction, sampling points using generative-based methods rather than uncertainty-driven approaches. In addition, these methods only concatenate visual and tactile features, failing to fully leverage the complementary advantages of these two modalities, resulting in unsatisfactory reconstruction results. To address this issue, leveraging the open-source GelSight tactile sensor, this article proposes a novel method called UDI-3DVT, which reconstructs 3-D shapes by combining visual point clouds and multiple tactile images. Specifically, we explore the uncertainty of point clouds, performing additional sampling on high-uncertainty edge points, and propose an attention-based point cloud sampling method. Then, the framework leverages the efficiency of convolutional neural network (CNN) in image processing and the transformer's superiority in context modeling by using CNN for tactile features and transformer for visual features. A feature fusion module based on 1 × 1 convolution is designed to aggregate features from two branches. By gradually adding tactile features, we achieve incremental visual-tactile integration. Furthermore, since the contact-based perception of tactile sensors may cause slight deformations in objects, UDI-3DVT supports the reconstruction of both rigid and deformable objects. Extensive experiments demonstrate that our proposed framework outperforms current state-of-the-art (SOTA) methods, especially the Chamfer distance (CD) metric increased by 14.8%. © 2001-2012 IEEE.

关键词： Image reconstruction

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Unsupervised Outlier Detection with Reinforced Noise Discriminator

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ACM Transactions on Intelligent systems and technology 2025年第2期16卷 1-26页

作者： Zhang, Zhongping Liu, Daoheng Zhu, Jinwei Wu, Youxi Hebei Key Laboratory of Computer Virtual Technology and System Integration School of Information Science and Engineering Yanshan University Qinhuangdao China School of Information Science and Engineering Yanshan University Qinhuangdao China School of Artificial Intelligence Hebei University of Technology Tianjin China

Outlier detection is one of the hot topics in the field of machine learning and data mining. At present, there are many kinds of outlier detection algorithms. The accuracies of traditional outlier detection algorithms are often affected by unique parameters, and an increase in the amount of data and the dimensions of the data can seriously affect their efficiency and effectiveness. Methods based on generative adversarial networks (GANs) can solve the above problems, but they are unacceptable since the model often collapses during the training period. In this article, to solve the problems of curse of dimensionality and model collapse, we propose a novel reinforced noise discriminator (RND) method for unsupervised outlier detection in tabular data. We consider outlier detection as a binary classification problem. Thus, we apply a learnable reinforced discriminator and generate a large number of potential outliers with a uniform distribution and potential outliers that are close to the original data that are used as a negative sample to train the discriminator, which learns the distribution of the original data to detect outliers. We empirically compare the proposed approach with ten state-of-the-art outlier detection methods on both synthetic and real-world tabular datasets. The experimental results show that RND outperforms its competitors in the majority of cases. The codes used to perform the experiments described in this article are available at https://***/urlhearts/r-n-d. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive Control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Attack and Defense Game with Intuitionistic Fuzzy Payoffs in Infrastructure Networks

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Tsinghua science and technology 2025年第1期30卷 384-401页

作者： Yibo Dong Jin Liu Jiaqi Ren Zhe Li Weili Li Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China

Due to our increasing dependence on infrastructure networks,the attack and defense game in these networks has draw great concerns from security ***,when it comes to evaluating the payoffs in practical attack and defense games in infrastructure networks,the lack of consideration for the fuzziness and uncertainty of subjective human judgment brings forth significant challenges to the analysis of strategic interactions among decision *** paper employs intuitionistic fuzzy sets(IFSs)to depict such uncertain payoffs,and introduce a theoretical framework for analyzing the attack and defense game in infrastructure networks based on intuitionistic fuzzy *** take the changes in three complex network metrics as the universe of discourse,and intuitionistic fuzzy sets are employed based on this universe of discourse to reflect the satisfaction of decision *** employ an algorithm based on intuitionistic fuzzy theory to find the Nash equilibrium,and conduct experiments on both local and global *** show that:(1)the utilization of intuitionistic fuzzy sets to depict the payoffs of attack and defense games in infrastructure networks can reflect the unique characteristics of decision makers’subjective preferences.(2)the use of differently weighted proportions of the three complex network metrics has little impact on decision makers’choices of different strategies.

关键词： infrastructure networks attack and defense game intuitionistic fuzzy set Nash equilibrium

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Identification algorithm of low-count energy spectra under short-duration measurement based on heterogeneous sample transfer

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Nuclear science and Techniques 2025年第3期36卷 12-26页

作者： Hao-Lin Liu Hai-Bo Ji Jiang-Mei Zhang Jing Lu School of Information Engineering Southwest University of Science and TechnologyMianyang 621010China Department of Automation University of Science and Technology of ChinaHefei 230026China Fundamental Science on Nuclear Wastes and Environment Safety Laboratory Southwest University of Science and TechnologyMianyang 621010China School of Automation and Information Engineering Sichuan University of Science and EngineeringZigong 643000China

In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical *** issues can lead to potential failures in peak-searching-based identification *** address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification *** experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma *** only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain *** proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.

关键词： Radionuclide identification Low-count Gamma energy spectral analysis Heterogeneous Transfer learning

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Observation of memristive behavior in PDMS-glass nanofluidic chip

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Nano Research 2025年第2期18卷 725-732页

作者： Weiling Sun Yike Xiao Pingyuan Yan Fei Sun Xuan Zhang Chuanxiang Sheng Qi Wang Yefeng Yu School of Microelectronics Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of New Membrane Materials Ministry of Industry and Information TechnologySchool of Environmental and Biological EngineeringNanjing University of Science and TechnologyNanjing 210094China Department of Optical Science and Engineering School of Information Science and TechnologyFudan UniversityShanghai 200433China

Nanofluidic memristors,which use ions in electrolyte solutions as carriers,have been developed rapidly and brought new opportunities for the development of neuromorphic *** the transport and accumulation of ions in nanochannels to process information is an endeavor to realize the nanofluidic *** this study,we report a new nanofluidic memristor,which is a polydimethylsiloxane(PDMS)-glass chip with two platinum(Pt)electrodes and well-aligned multi-nanochannels within PDMS for ion enrichment and *** device not only exhibits typical bipolar memristive behavior and ion current rectification(ICR)but also demonstrates excellent endurance,maintaining stable performance after 100 sweep *** systematically investigate the key factors affecting ion transport behavior in this *** results show that the ICR ratio of the current-voltage(I-V)hysteresis curves decreases with increasing scan rate and solution *** potential measurements are introduced to reveal that the PDMS surface carries more negative charges in higher pH solutions,resulting in more pronounced memristive and ICR ***,our memristor can simulate short-term synaptic plasticity,such as paired-pulse facilitation(PPF)and paired-pulse depression(PPD),with a relatively low energy consumption of 12 pJ per spike per ***,the inherent accessibility and robustness of our nanofluidic memristors facilitate the optimization of device structure and *** important observations and investigations lay a foundation for advancing energy-saving and efficient neuromorphic computing.

关键词： polydimethylsiloxane(PDMS)-glass chip nanofluidic memristor nanochannel short-term synaptic plasticity(STP)

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