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arXiv 2025年

作者： Su, Han Shankar, Saumya Pinisetty, Srinivas Roop, Partha S. Zhan, Naijun Institute of Software CAS & University of CAS Beijing China Department of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand Indian Institute of Technology School of Electrical Sciences Bhubaneswar India The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand Peking University School of Computer Science Institute of Software CAS Beijing China

Cyber-Physical Systems (CPSs), especially those involving autonomy, need guarantees of their safety. Runtime Enforcement (RE) is a lightweight method to formally ensure that some specified properties are satisfied over the executions of the system. Hence, there is recent interest in the RE of CPS. However, existing methods are not designed to tackle specifications suitable for the hybrid dynamics of CPS. With this in mind, we develop runtime enforcement of CPS using properties defined in Signal Temporal Logic (STL). In this work, we aim to construct a runtime enforcer for a given STL formula to minimally modify a signal to satisfy the formula. To achieve this, the STL formula to be enforced is first translated into a timed transducer, while the signal to be corrected is encoded as timed words. We provide timed transducers for the temporal operators until and release noting that other temporal operators can be expressed using these two. Our approach enables effective enforcement of STL properties for CPS. A case study is provided to illustrate the approach and generate empirical evidence of its suitability for CPS. © 2025, CC BY-NC-ND.

关键词： Transducers

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Resolution-Preserving Multi-Scale Network for 5G-LTE Spectrogram-Based Spectrum Sensing

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IEEE Wireless Communications Letters 2025年

作者： Nguyen, Huu-Tai Dang, Hai-Trang Phuoc Pham, Quoc-Viet Huynh-The, Thien Ho Chi Minh City University of Technology and Education Department of Computer and Communications Engineering Ho Chi Minh City71307 Viet Nam Trinity College Dublin School of Computer Science and Statistics Dublin 2 D02 PN40 Ireland

This paper introduces PRMNet, an advanced deep model with a convolutional architecture for segmenting wireless signals in time-frequency occupancy spectrograms, designed to enhance spectrum sensing accuracy. PRMNet features a resolution-preserving architecture combined with a multi-scale feature extraction module to capture features across multiple resolution levels while preserving the original resolution of input spectrograms. This design facilitates an effective extraction of both local and global spectral features, thus robustly analyzing wideband spectrograms under diverse signal and challenging channel conditions. Experimental results reveal that PRMNet outperforms state-of-the-art models, such as FCN, U-Net, U-Net++, DeepLabV3+, and SegFormer, with a mean accuracy of 92.44%, a mean IoU of 87.6%, a mean F1 score of 93.35%, and a mean precision of 94.65%. Despite its compact architecture with only 14.5M parameters, PRMNet delivers remarkable performance, thus offering a promising solution for intelligent spectrum sensing in next-generation wireless communication systems. © 2025 IEEE. All rights reserved.

关键词： 5G mobile communication systems

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Technical Indicator-Driven Stock Prediction: A LightGBM and ARIMA Perspective

Technical Indicator-Driven Stock Prediction: A LightGBM and ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： S Yuvaraj J Chenni Kumaran R Benschwartz M Pradeepa Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai TamilNadu India Department of Electronics and Communication Engineering Mar Ephraem College of Engineering and Technology Marthandam Tamil Nadu India Department of Electronics and Communication Engineering SNS College of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The study examined the ability of a Light Gradient Boosting Machine (LightGBM) to predict stock prices with Exponential Moving Averages (EMA_5) and Simple Moving Averages (SMA_5) as primary Technical Indicators. Due to the autocorrelation in time series data, the adjusted closing prices were considered to run the model with several indicators effectively. These adjusted prices are subsequently used to compute the EMA_5 and SMA_5 as features in the model, to smooth price predictions and provide a clearer understanding of short-term market movements by identifying emerging patterns and trends with greater precision. The performance of LightBGMs will also be evaluated against ARIMA using statistical measures, including Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicated that for most instances, LightGBM outperforms ARIMA, producing R2 scores higher than 99%. It showcases the possibility of LightGBM as a robust stock market prediction model when it comes to the apparent complexities of market data. Future work will further extend the model's features to improve its already stunning, reliable prediction accuracy.

关键词： Computational modeling Time series analysis Measurement uncertainty Predictive models Boosting Market research Data models Reliability Internet of Things Stock markets

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Improving the electrochemical performance of LiNi0.5Co0.2Mn0.3O2 via surface modification with Li1.3Al0.3Ti1.7(PO4)3 coating

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Electrochemistry Communications 2025年 176卷

作者： Yao, Nianchun Zhang, Min He, Yulin Cao, Keyin Li, Ying Wang, Ziqiang School of Intelligent Engineering Technology Jiangsu Vocational College of Finance & Economics Jiangsu Huai'an223003 China School of Materials Science and Engineering/Laboratory for microstructures Shanghai University Shanghai200072 China School of Computer and Communication Jiangsu Vocational College of Electronics and Information Jiangsu Huaian223003 China Huai'an Technology Institute of Advanced Energy Materials 19 Meigao Road Huai'an223005 China

This paper presents a simplified process for synthesizing LiNi0.5Co0.2Mn0.3O2 (NCM523) coated with Li1.3Al0.3Ti1.7(PO4)3 (LATP) to optimize electrochemical characteristics by adjusting LATP content adjustment. LATP, as a superior Li-ion conductive layer, reduces polarization and enhances cycling performance at 50 °C. X-ray diffraction (XRD) confirms that all samples maintain a stable alpha-NaFeO2 layered structure. Performance tests of half and full batteries at room temperature and 50 °C show that the 2 wt% LATP-coated sample exhibits the best electrochemical performance. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) show that the LATP-coated sample has higher lithium ion diffusion coefficients and lower charge transfer resistance, which improves rate capability. © 2025

关键词： Cyclic voltammetry

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Evolving temporal convolutional neural networks for modeling turntable servo systems

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Applied Soft Computing 2025年 172卷

作者： Xie, Cheng Xue, Bing Zhang, Mengjie Chen, Songlin Liu, Yang Control and Simulation Center School of Astronautics Harbin Institute of Technology Heilongjiang Harbin150001 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110169 China

Turntable servo systems are important experimental equipment used for semi-physical simulation and testing of aircraft, which have very strict requirements for tracking performance. To achieve high-precision servo performance, some advanced model-based control methods have been used recently, but the control performance is greatly influenced by the accuracy of the model. Therefore, this paper proposes evolving temporal convolutional networks (TCNs) for accurately modeling turntable servo systems. Considering the advantages of TCNs in capturing local dependencies in sequence data and parallel computing, a TCN is adopted to compensate the unmodeled dynamics to significantly improve the accuracy of the existing dynamics model. Then, an evolutionary algorithm (EA) based architecture optimization algorithm for the above TCN model is proposed, and the network performance is continuously improved by simulating the population evolution in nature. Finally, the weight inheritance method is utilized to evaluate the performance of individuals during the evolution process to effectively improve the execution efficiency of the whole algorithm. In addition, the subnets inherited from the supernet meet the real-time requirements of the actual tasks by setting real-time constraints on the supernet. The experimental results demonstrate the superiority of the proposed model against the peer competitors in terms of the prediction performance. In addition, the proposed optimization algorithm also shows higher execution efficiency as verified by ablation experiments. © 2025 Elsevier B.V.

关键词： Evolutionary algorithms (EAs) System identification and modeling Temporal convolutional networks (TCNs) Turntable servo systems

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Power-Measurement-Based Channel Autocorrelation Estimation for IRS-Assisted Wideband Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Sun, He Zhu, Lipeng Mei, Weidong Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications 611731 China The Chinese University of Hon g Kong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Channel state information (CSI) is essential to the performance optimization of intelligent reflecting surface (IRS)-aided wireless communication systems. However, the passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed practical challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To tackle the above challenge, we propose a novel neural network (NN)-empowered IRS channel estimation and passive reflection design framework for the wideband orthogonal frequency division multiplexing (OFDM) communication system based only on the user’s reference signal received power (RSRP) measurements with time-varying random IRS training reflections. As RSRP is readily accessible in existing communication systems, our proposed channel estimation method does not require additional pilot transmission in IRS-aided wideband communication systems. In particular, we show that the average received signal power over all OFDM subcarriers at the user terminal can be represented as the prediction of a single-layer NN composed of multiple subnetworks with the same structure, such that the autocorrelation matrix of the wideband IRS channel can be recovered as their weights via supervised learning. To exploit the potential sparsity of the channel autocorrelation matrix, a progressive training method is proposed by gradually increasing the number of subnetworks until a desired accuracy is achieved, thus reducing the training complexity. Based on the estimates of IRS channel autocorrelation matrix, the IRS passive reflection is then optimized to maximize the average channel power gain over all subcarriers. Numerical results indicate the effectiveness of the proposed IRS channel autocorrelation matrix estimation and passive reflection design under wideband channels, which can achieve significant performance improvement compared to the existing IRS re

关键词： Orthogonal frequency division multiplexing

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Low-rank reduced biquaternion tensor ring decomposition and tensor completion

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Applied Mathematics and Computation 2025年 504卷

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence at Sun Yat-sen University the Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025 Elsevier Inc.

关键词： Numerical methods

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Waveform and Filter Design for Integrated Sensing and Communication Against Signal-dependent Modulated Jamming

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IEEE Transactions on Vehicular technology 2025年

作者： Zhou, Yu Shi, Qiao Zhou, Zhengchun Liu, Zilong Fan, Pingzhi Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Southwest Jiaotong University Key Laboratory of Information Coding and Wireless Communications Chengdu611756 China

This paper focuses on an integrated sensing and communication (ISAC) system in the presence of signal-dependent modulated jamming (SDMJ). Our goal is to suppress jamming while carrying out simultaneous communications and sensing. We minimize the integrated sidelobe level (ISL) of the mismatch filter output for the transmitted waveform and the integrated level (IL) of the mismatch filter output for the jamming, under the constraints of the loss in-processing gain (LPG) and the peak-to-average power ratio (PAPR) of the transmitted waveform. Meanwhile, the similarity constraint is introduced for information-bearing transmit waveform. We develop a decoupled majorization minimization (DMM) algorithm to solve the proposed multi-constrained optimization problem. In contrast to the existing approaches, the proposed algorithm transforms the difficult optimization problem involving two variables into two parallel sub-problems with one variable, thus significantly speeding up the convergence rate. Furthermore, fast Fourier transform (FFT) is introduced to compute the closed-form solution of each sub-problem, giving rise to a greatly reduced computation complexity. Simulation results demonstrate the capabilities of the proposed ISAC system which strikes a proper trade-off among sensing and jamming suppression. © 1967-2012 IEEE.

关键词： Constrained optimization

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Co-evolution-based Metal-binding Residue Prediction with Graph Neural Networks

arXiv

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arXiv 2025年

作者： Rastegari, Sayedmohammadreza Tabakhi, Sina Liu, Xianyuan Sang, Wei Lu, Haiping Faculty of Computer Engineering University of Isfahan Isfahan Iran School of Computer Science Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Basic Medical Sciences Institute of Medical Technology Shanxi Medical University Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

In computational structural biology, predicting metal-binding sites and their corresponding metal types is challenging due to the complexity of protein structures and interactions. Conventional sequence- and structure-based prediction approaches cannot capture the complex evolutionary relationships driving these interactions to facilitate understanding, while recent co-evolution-based approaches do not fully consider the entire structure of the co-evolved residue network. In this paper, we introduce MBGNN (Metal-Binding Graph Neural Network) that utilizes the entire co-evolved residue network and effectively captures the complex dependencies within protein structures via graph neural networks to enhance the prediction of co-evolved metal-binding residues and their associated metal types. Experimental results on a public dataset show that MBGNN outperforms existing co-evolution-based metal-binding prediction methods, and it is also competitive against recent sequence-based methods, showing the potential of integrating co-evolutionary insights with advanced machine learning to deepen our understanding of protein-metal interactions. The MBGNN code is publicly available at https://***/SRastegari/MBGNN. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Reconfigurable Intelligent Surface-Aided Covert Communications: A Multi-User Scenario

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IEEE Transactions on Vehicular technology 2025年

作者： Hu, Xiaoyan Zhao, Pengze Xiao, Han Zheng, Tong-Xing Wang, Wenjie Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering United Kingdom

This paper explores a multi-user covert communication scenario with the assistance of a reconfigurable intelligent surface (RIS). Except a covert user monitored by the warden, there are multiple public users existing in the service area. For the purpose of evaluating the efficacy of the covert communication system, a covert constraint is derived measuring the minimum detection error probability of the warden through the Kullback-Leibler (KL) divergence. Then an optimization problem is formulated to maximize the convert rate, while ensuring the constraints of covertness, transmit power and quality of service (QoS) for public users. A two-step iterative algorithm jointly designing the active and passive beamforming, is put forth to efficiently resolve this non-convex optimization problem. Additionally, the numerical results clearly demonstrate that the proposed RIS-assisted scheme can significantly enhance the covert communication performance on the premise of guaranteeing the public communication performance of the system. © 1967-2012 IEEE.

关键词： Convex optimization

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