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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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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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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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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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DCM-based dynamic stable walking under terrain-induced time-varying disturbances for humanoid robots

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science China(Technological sciences) 2025年第4期68卷 105-117页

作者： Yan LUO Chao LIU Jianhua WU Zhenhua XIONG Han DING State Key Laboratory of Mechanical System and Vibration School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China

When humanoid robots attempt to walk on terrain such as shaking platforms,time-varying disturbances are introduced to the support *** abrupt changes of inclination angle can cause the robot to lose balance upon landing,presenting significant challenges for balance control *** address this issue,we propose a novel divergent component of motion(DCM)-based time-varying disturbance walking(DCM-TVDW)*** method allows the robot to walk on rugged surfaces and helps to maintain dynamic balance when subjected to large time-varying *** the DCM-TVDW control method,we first adjust the robot's center of mass and stride height to adapt to transitions between different terrain types via a variable height stabilization method,and hold these quantities constant as base *** then combine DCM with the N-step capturability *** combination allows for dynamic balance through multi-step adjustments from the initially unstable region,thereby extending the robots stability *** and experimental results demonstrate that the DCM-TVDW method enables the SJ-Bruce robot to traverse a dynamically shaking platform with an inclination angle of approximately 22°.

关键词： humanoid robot dynamic balance control capturability time-varying disturbance

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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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Metal-Frame Slot Antenna with a Small Ground Clearance for the Wi-Fi 6E Operation in Laptop Applications

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IEEE Transactions on Antennas and Propagation 2025年第5期73卷 3394-3399页

作者： Wu, Jiahao Yang, Guo-Min Wang, Yan Yan, Na Jin, Ya-Qiu Fudan University Key Laboratory for Information Science of Electromagnetic Waves School of Information Science and Technology Shanghai200433 China Fudan University State Key Laboratory of ASIC and System School of Microelectronics Shanghai200433 China

In this communication, a metal-frame slot antenna with a very small ground clearance for laptop is proposed. Regarding the antenna structure, it is an asymmetrical modified inverted T-shaped slot (AMITS) structure, which includes a long step slot arm, a short slot arm and a loop slit. The AMITS antenna is integrated with the metal casing connected with an inverted L-shaped strip, covering the 2.4/5/6 GHz frequency bands with four resonant modes excited. The antenna is analyzed and a prototype is fabricated and measured. The proposed AMITS antenna features a very-small ground clearance measuring merely 1.2 mm, covering a-10 dB impedance bandwidth of 160 MHz (2.33-2.49 GHz) and 2.4 GHz (4.92-7.32 GHz) with a peak efficiency of 70%. The low-profile, simple-structure AMITS antenna shows prospect for the narrow-bezel laptop in Wi-Fi 6E applications. © 1963-2012 IEEE.

关键词： Wi-Fi

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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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Temporal-spectral correlation dynamics of Raman random fiber laser

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science China(information sciences) 2025年第4期68卷 166-176页

作者： Longqun NI Yifei QI Xingyu BAO Jing ZHANG Pan WANG Han WU Zinan WANG Key Laboratory of Optical Fiber Sensing and Communications University of Electronic Science and Technology of China College of Electronics and Information Engineering Sichuan University

Raman random fiber laser(RRFL) possesses rich physical properties of spectral, temporal, and spatial domains due to its unique feedback mechanism and complex nonlinear effects. Characterizing and controlling the microscopic evolution dynamics of RRFL are crucial to driving breakthrough advances in fields such as inertial confinement fusion and fundamental physics. In this work, a novel experimental and theoretical analysis of the evolution of the temporal spectral correlations of the RRFL in the transition and steady states is conducted. In the transitional state, the microscopic dynamics of the RRFL excitation process is revealed comprehensively: the temporal-correlation growth curve contrasts with that of resonant-cavity lasers, and the formation and degradation of spectral correlation are observed. In the steady state, the overall spectrum is characterized by partial correlation, and the correlation characteristics of RRFL mainly originate from the spectral random spikes, which offers a novel dimension for the precise control of RRFL correlation. This work provides new insights into underlying physical properties of continuous broadband lasers, offering key guidance for laser design, control, and applications.

关键词： random fiber laser microscopic characteristics temporal-spectral correlation random spikes nonlinear optics

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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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