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Intelligent integrated sensing and communication:a survey

Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Sequential Design Process of a 350-kW Class Dual Three-Phase IPMSM for a Wheeled Armored Vehicle

IEEE Open Journal of Industry Applications

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IEEE Open Journal of Industry Applications 2025年 6卷 237-248页

作者： Son, Ji-Chang Kwon, Min-Su Lim, Dong-Kuk University of Ulsan Department of Electrical Electronics and Computer Engineering Ulsan44610 Korea Republic of

Optimal design of a traction motor is a complex process, as various requirements and constraints need to be satisfied. In addition, consideration of various physical aspects, such as stress and heat, is necessary to ensure the stability of the motor, including mechanical rigidity and insulation breakdown. In this article, to derive the optimal design of an interior permanent magnet synchronous motor (IPMSM), a novel sequential design process consisting of conceptual design, detailed design, and optimal design is proposed. The conceptual design enables the rapid execution of multiple case studies, as static electromagnetic analysis is used. The overall geometric parameters are determined through electromagnetic analysis in the detailed design stage and an initial model that satisfies the requirements is derived. Finally, in the optimal design stage, the optimal model is quickly derived using a machine learning method, and the stability of the model is examined through multiphysics analysis. To validate the applicability to the practical motor, design optimization for a 350-kW class dual three-phase IPMSM for a wheeled armored vehicle is conducted, and the feasibility of the proposed method is verified based on the experimental results of the manufactured prototype. © 2020 IEEE.

关键词： Armored vehicles

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PAFWF-EEGC Net: parallel adaptive feature weight fusion based on EEG-dynamic characteristics using channels neural network for driver drowsiness detection

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Signal, Image and Video Processing 2025年第7期19卷 1-22页

作者： Abdulwahhab, Ali H. Myderrizi, Indrit Yurdakul, Muhammet Mustafa Electrical and Computer Engineering Department Altinbas University Istanbul Turkey Electrical and Electronics Engineering Department Istinye University Istanbul Turkey Computer Engineering Department Istanbul Gelisim University Istanbul Turkey

Drowsy driving is considered one of the most dangerous causes of road accidents and deaths worldwide. Drivers’ concentration is directly affected by fatigue, which affects their reaction time, reducing their attention and decision-making ability on the road. This can often lead to dangerous situations. With the development of Human computer Interface systems and the rise of intelligent transportation systems, examining the effects of driver fatigue has become more critical, and research aimed at reducing the risk of fatigue-related accidents has gained importance. For this purpose, this study proposes a Parallel Adaptive Feature Weight Fusion based on EEG-Dynamic Characteristics using Channels Neural Network (PAFWF-EEGC Net) to detect the driver drowsiness condition. Two signal processing techniques are used to extract EEG dynamic features: first, Continuous Wavelet Transform (CWT) to capture the spectral-temporal features by accurately estimating both time and frequency localizations, and second, Fast Fourier Transform (FFT)—Power Spectrum Density (PSD) to convert the signals from the time domain to the frequency domain and show the distribution of signal power over frequency. These extracted dynamic features are passed to Attention channels and Parallel Adaptive Feature Fusion to integrate the most relevant feature channels to detect mental state. Furthermore, three processing dataset scenarios and cross-validation techniques are used to validate the Net. The Net showed excellent performance through ninefold/3rd scenario by achieving 98% detection accuracy, and 84%, 88.75%, 93.8% average detection accuracy through 1st, 2nd, 3rd scenarios respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Impact of Different Integration Times on Distributions of Rain Rates for Predictions of Rain Attenuation

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Progress in Electromagnetics Research C 2025年 154卷 229-238页

作者： Hassan, Mohammad Rofiqul Rafiqul, Islam Md. Habaebi, Mohamed Hadi Suriza, Ahmad Zabidi Badron, Khairayu Budalal, Asma Ali Mahfuz, Md. Mahmudul Hasan Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Jalan Gombak Kuala Lumpur53100 Malaysia College of Electrical and Electronics Technology Benghazi Libya Department of Electrical and Computer Engineering Concordia University Canada

All wireless communication systems are moving towards higher and higher frequencies day by day which are severely attenuated by rains in outdoor environment. To design a reliable RF system, an accurate prediction method of rain attenuation is established and used globally based on local rain intensity measurement. Required rain intensity used for attenuation prediction is generally measured at a point with 1-min integration time or converted from higher integration time to 1-min. Recent measurements of rain intensity with a 10-second integration time indicate that intensity is not uniform over a 1-minute duration. Consequently, the statistics of rain intensity distribution and attenuation predictions are influenced by measurements with integration times shorter than 1 minute. It has been established that an integration time of 0.01% provides the optimal fit for actual rain rate data. This paper presents the rain intensity distributions from data measured with 2-min, 1-min, 30-sec, 20-sec, and 10-sec integration times, and it has impact on rain rate distributions as well as rain attenuation predictions. © 2025, Electromagnetics Academy. All rights reserved.

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Power Electronic Converters in Inductive Wireless Charging Applications for Electric Transportation

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IEEE Journal of Emerging and Selected Topics in Power electronics 2025年第2期13卷 2647-2683页

作者： Bharatiraja, C. Mahesh, Aganti Lehman, Bradley Department of Electrical and Electronics Engineering Kattankulathur College of Engineering Chengalpattu Chennai603203 India Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States

Wireless charging system (WCS) for electric vehicles (EVs) has the potential to revolutionize traditional plug-in charging methods. This technology employs different methods. Among these, resonant inductive power transfer (R-IPT) stands out in EV applications for its high efficiency and lower operating frequencies compared to others. The role of power electronic converters (PECs) in achieving high frequency and resonance operation is vital, with switches and compensators playing crucial roles in resonant converter design. This article emphasizes the significance of PECs, explores the factors influencing converters selection and their classifications, and provides an overview of various PECs at different stages of wireless power transfer (WPT) systems, along with their characteristics. Additionally, it delves into wide bandgap (WBG) devices and different semiconductor switches, discussing their roles in R-IPT systems, as well as their advantages and disadvantages. This article also outlines the characteristics, features, and limitations of various overview compensation topologies, serving as a valuable resource for researchers seeking to develop new topologies for wireless charging applications. A comprehensive discussion on future challenges, development trends, and prospects is provided. © 2013 IEEE.

关键词： Charging stations

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Detection of Economic Crises With Language Models and Comparative Analysis of Simple Time Series Analysis Models and Machine Learning Algorithms on the Stock Market

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IEEE Access 2025年 13卷 83254-83273页

作者： Kotan, Kurban Kotan, Bayram Kirisoglu, Serdar Duzce University Department of Electrical Electronics and Computer Engineering Düzce81620 Turkey Duzce University Engineering Faculty Department of Computer Engineering Düzce81620 Turkey

This study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series analysis and machine learning models in financial markets before and during the economic crises in order to select the best model. The data used in the research was collected based on the economic crises that occurred in Turkey in December 2021. The aim is to identify an economic crises period by using language representation models for economic news between August 2021 and January 2022. After identifying the economic crises period, short term (1 day), medium term (15 days) and long term (30 days) forecasts were made for the index of thirty companies with the highest trading volume (BIST30) of Borsa Istanbul between 01/01/2021 and 31/12/2021 and performance comparisons were made between the models. The aim is to develop an effective "smart, automatic crises detection and forecasting model selection application". The CHIT algorithm introduced in the study is a new missing data filling algorithm used in time series forecasting comparisons. Since the CHIT algorithm has a high impact on the model performance, this algorithm is used in the pre-processing step and comparisons are made. © 2013 IEEE.

关键词： Natural language processing systems

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Efficient 2 × 2 multimode switch employing Sb2Se3 phase change material for enhanced mode division multiplexing systems

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Optical and Quantum electronics 2025年第5期57卷 1-20页

作者： Atri, Ali Zarifkar, Abbas Department of Communication and Electronics School of Electrical and Computer Engineering Shiraz University Shiraz Iran

Mode division multiplexing (MDM) technology represents a significant advancement in high-capacity optical data transmission in photonics integrated circuits (PICs). Among the critical components in MDM architecture are multimode switches that enable signals to be routed simultaneously along different waveguides. In this study, we present a two-mode 2×2 optical switch utilizing an asymmetric directional coupler with Sb2Se3 as the phase change material which is designed at an ultra-compact footprint of 53 μm × 6 μm. This switch effectively routes the first two transverse magnetic modes simultaneously over a broad bandwidth within the third telecommunications window, specifically from 1535 to 1565 nm. Our 3D finite-difference time-domain simulation results indicate that the switch exhibits a low insertion loss of 0.78 dB and a crosstalk level of -7.98 dB in the "on" state across the specified bandwidth. Furthermore, in the "off" state, the switch maintains favorable performance characteristics, achieving insertion loss and crosstalk values of less than 0.41 dB and -15.69 dB throughout the operational bandwidth. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Directional couplers

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A Detailed Comparative Analysis of Automatic Neural Metrics for Machine Translation: BLEURT & BERTScore

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2025年第1期6卷 658-668页

作者： Mukherjee, Aniruddha Hassija, Vikas Chamola, Vinay Gupta, Karunesh Kumar KIIT School of Computer Engineering Bhubaneshwar India BITS-Pilani Department of Electrical and Electronics Engineering Pilani Campus Rajasthan India

Bleurt a recently introduced metric that employs Bert, a potent pre-trained language model to assess how well candidate translations compare to a reference translation in the context of machine translation outputs. While traditional metrics like Bleu rely on lexical similarities, Bleurt leverages Bert's semantic and syntactic capabilities to provide more robust evaluation through complex text representations. However, studies have shown that Bert, despite its impressive performance in natural language processing tasks can sometimes deviate from human judgment, particularly in specific syntactic and semantic scenarios. Through systematic experimental analysis at the word level, including categorization of errors such as lexical mismatches, untranslated terms, and structural inconsistencies, we investigate how Bleurt handles various translation challenges. Our study addresses three central questions: What are the strengths and weaknesses of Bleurt, how do they align with Bert's known limitations, and how does it compare with the similar automatic neural metric for machine translation, BERTScore? Using manually annotated datasets that emphasize different error types and linguistic phenomena, we find that Bleurt excels at identifying nuanced differences between sentences with high overlap, an area where BERTScore shows limitations. Our systematic experiments, provide insights for their effective application in machine translation evaluation. © 2020 IEEE.

关键词： computer aided language translation

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Binary feature mask optimization for feature selection

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Neural Computing and Applications 2025年第6期37卷 5155-5167页

作者： Lorasdagi, Mehmet E. Turali, Mehmet Y. Kozat, Suleyman S. Department of Electrical and Electronics Engineering Bilkent University Ankara06800 Turkey Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles United States

We investigate feature selection problem for generic machine learning models. We introduce a novel framework that selects features considering the outcomes of the model. Our framework introduces a novel feature masking approach to eliminate the features during the selection process, instead of completely removing them from the dataset. This allows us to use the same machine learning model during feature selection, unlike other feature selection methods where we need to train the machine learning model again as the dataset has different dimensions on each iteration. We obtain the mask operator using the predictions of the machine learning model, which offers a comprehensive view on the subsets of the features essential for the predictive performance of the model. A variety of approaches exist in the feature selection literature. However, to our knowledge, no study has introduced a training-free framework for a generic machine learning model to select features while considering the importance of the feature subsets as a whole, instead of focusing on the individual features. We demonstrate significant performance improvements on the real-life datasets under different settings using LightGBM and multilayer perceptron as our machine learning models. Our results show that our methods outperform traditional feature selection techniques. Specifically, in experiments with the residential building dataset, our general binary mask optimization algorithm has reduced the mean squared error by up to 49% compared to conventional methods, achieving a mean squared error of 0.0044. The high performance of our general binary mask optimization algorithm stems from its feature masking approach to select features and its flexibility in the number of selected features. The algorithm selects features based on the validation performance of the machine learning model. Hence, the number of selected features is not predetermined and adjusts dynamically to the dataset. Additionally, we openly s

关键词： Mean square error

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LSTM-SAC reinforcement learning based resilient energy trading for networked microgrid system

AIMS Electronics and Electrical Engineering

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AIMS electronics and electrical engineering 2025年第2期9卷 165-191页

作者： Sharma, Desh Deepak Bansal, Ramesh C. Department of Electrical Engineering MJP Rohilkhnad University Bareilly India Electrical Engineering Department University of Sharjah Sharjah United Arab Emirates Department of Electrical Electronics & Computer Engineering University of Pretoria Pretoria South Africa

On the whole, the present microgrid constitutes numerous actors in highly decentralized environments and liberalized electricity markets. The networked microgrid system must be capable of detecting electricity price changes and unknown variations in the presence of rare and extreme events. The networked microgrid system comprised of interconnected microgrids must be adaptive and resilient to undesirable environmental conditions such as the occurrence of different kinds of faults and interruptions in the main grid supply. The uncertainties and stochasticity in the load and distributed generation are considered. In this study, we propose resilient energy trading incorporating DC-OPF, which takes generator failures and line outages (topology change) into account. This paper proposes a design of Long Short-Term Memory (LSTM) - soft actor-critic (SAC) reinforcement learning for the development of a platform to obtain resilient peer-to-peer energy trading in networked microgrid systems during extreme events. A Markov Decision Process (MDP) is used to develop the reinforcement learning-based resilient energy trade process that includes the state transition probability and a grid resilience factor for networked microgrid systems. LSTM-SAC continuously refines policies in real-time, thus ensuring optimal trading strategies in rapidly changing energy markets. The LSTM networks have been used to estimate the optimal Q-values in soft actor-critic reinforcement learning. This learning mechanism takes care of the out-of-range estimates of Q-values while reducing the gradient problems. The optimal actions are decided with maximized rewards for peer-to-peer resilient energy trading. The networked microgrid system is trained with the proposed learning mechanism for resilient energy trading. The proposed LSTM-SAC reinforcement learning is tested on a networked microgrid system comprised of IEEE 14 bus systems. ©2025 the Author(s), licensee AIMS Press.

关键词： Stochastic systems

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