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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qincheng Lv Xiaofeng Liu Jie Li Rongrong Ni Pujun Xue Siyang Song College of Information Science and Engineering Hohai University Changzhou China College of Artificial Intelligence and Automation Hohai University Changzhou China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China School of Microelectronics and Control Engineering Changzhou University Changzhou China School of Computer Science University of Exeter Exeter U.K.

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Facial reactions convey crucial emotional information and coordinating interpersonal relationships in human dyadic interactions. While existing Multiple Appropriate Facial Reaction Generation (MAFRG) methods focus on generating multiple reasonable facial reactions, none of these approaches combines 2D and 3D facial behaviour information nor account for the influence of individuals’ facial identities, leading to inconsistencies in the generated facial reactions and limited capability in capturing subtle variations in facial depth and expression dynamics. This paper proposes a novel Hierarchical Multimodal Decoupling-Fusion (HMDF) framework that decouples 3D facial identity from expression behaviors, eliminating identity-based interference in the reaction generation process, which are integrated with audio-visual features through a cross-attention mechanism. Experiments show that our framework achieved the enhanced diversity and synchrony in the generated facial reactions.

关键词： Three-dimensional displays Interference Signal processing Acoustics Speech processing

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H control for General Conformable Polynomial Fuzzy Models

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International Journal of Fuzzy Systems 2025年 1-10页

作者： Laajimi, Malek Gassara, Hamdi Rhaima, Mohamed Mchiri, Lassaad Makhlouf, Abdellatif Ben Laboratory of Sciences and Techniques of Automatic control & Computer Engineering National School of Engineering of Sfax Sfax Tunisia Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Panthéon-Assas University Paris II Paris France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

This research focuses on exploring the exponential $$H_\infty $$ stability of general conformable nonlinear system. In order to address the nonlinearity inherent in the system, a polynomial fuzzy (PF) method is employed. Modeling a general conformable nonlinear system within the polynomial framework reduces the number of fuzzy rules compared to the classical Takagi–Sugeno fuzzy (TSF). Furthermore, controlling such a complex system, which accounts for perturbations, employs a PF model instead TSF model to describe its nonlinear dynamics, and incorporates a general conformable derivative instead of an integer-order one, is significantly more challenging, and remains unaddressed in previous studies. In this paper, a PF controller is designed in the form of sum of squares (SOS) to enhance the resilience against perturbations and ensure the exponential stability of the proposed model. The proposed SOS can be solved numerically, and partially symbolically, using the recently developed SOSTOOLS. In order to ensure the $$H_\infty $$ performance, a generalized criterion is defined for the general conformable nonlinear system. To demonstrate the effectiveness of the proposed method, a numerical example is provided.

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm

Enhancing 6G-Satellite Network Integration with Artificial I...

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Learning and Technology Conference (L&T)

作者： Ibrahim Salah Aziza I. Hussein Mostafa Wageh Lotfy Mohamed Mourad Mabrook Control and Computer Department College of Engineering Almaaqal University Basrah Iraq College of Engineering Energy and Technology Research Center Effat University Saudi Arabia Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Space Communication Dept. Faculty of Navigation Science & Space Technology Beni-Suef University Beni-Suef Egypt

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future.

关键词： 6G mobile communication Wireless communication Smart cities Resource management Reliability Internet of Things Stakeholders Artificial intelligence Vehicle dynamics Satellite communications

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Enhanced wind energy conversion system performance using fast smooth second-order sliding mode control with neuro-fuzzy estimation and variable-gain robust exact output differentiator

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Applied Energy 2025年第PartA期377卷

作者： Ullah, Ameen Ullah, Safeer Rahman, Tanzeel Ur Sami, Irfan Rahman, Ata Ur Alghamdi, Baheej Pan, Jianfei College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Quaid-e-Azam College of Engineering & Technology Sahiwal57000 Pakistan College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China School of Electrical and Electronics Engineering Chung-Ang University Dongjak-gu Seoul06974 Korea Republic of Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Wind energy conversion systems (WECS) often face challenges due to the stochastic and intermittent nature of wind, leading to a mismatch between output power generation and fluctuating electrical load requirements. To effectively address this issue, an advanced Maximum Power Point Tracking (MPPT) strategy is crucial for maximizing power extraction. This study introduces a novel MPPT approach based on Fast Smooth Second-Order Sliding Mode control (FSSOSMC) to optimize power output from a 3 kW fixed-pitch variable speed WECS coupled with a permanent magnet synchronous generator (PMSG). To ensure robustness despite uncertain, nonlinear system parameters, an offline neuro-fuzzy algorithm utilizing the Takagi–Sugeno–Kang (TSK) fuzzy inference system is implemented. This method accurately estimates the nonlinear and uncertain components of the control input, enhancing the performance and robustness of the MPPT control techniques. Additionally, a Variable-Gain Robust Exact Output Differentiator (VG-REOD) is employed to accurately estimate the shaft speed, addressing issues related to speed estimation and missing derivative information. The proposed FSSOSMC-based MPPT strategy was benchmarked against super-twisting sliding mode control (STSMC) and feedback linearization control (FBLC) MPPT strategies under stochastic wind speed profiles, parameter variations, and wind speed fluctuations. The results show that the proposed method achieves a tracking accuracy of 98.2% and an overall efficiency of 98.9%, significantly outperforming STSMC (97.1% accuracy, 95.75% efficiency) and FBLC (95.4% accuracy, 93.82% efficiency). The FSSOSMC method also reduced the settling time to 7.879 s and the rise time to 1.062 s, with minimal overshoot of 10.022% and a steady-state error of 0.0015088. These results demonstrate superior tracking performance, high precision, rapid dynamic response, minimal chattering, and robust global performance. The efficacy of the proposed method is validated thro

关键词： Stochastic systems

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Unknown Input Observer Design of Switched Systems Based on Iterative Learning

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Journal of Systems Science & Complexity 2019年第3期32卷 875-887页

作者： CAO Wei SUN Ming College of Computer and Control Engineering Qiqihar University

For a class of discrete switched systems with unknown input, an unknown input observer design method is proposed under the premise of changes along time axis but no changes along iteration axis. This method applies the iterative learning control thought to the design of unknown input observer, construets the unknown input observer by introducing virtual input signal, and uses the error signal generated from the actual system output and the observer output to correct repetitively the virtual input, which gradually approxima tes the actual unknown input as the it erations increase. Moreover, the convergence of the observer is strictly proved based on contraction mapping theory, as well as the convergence condition is given. The theoretical analysis indicates that designed unknown input observer can accurately estimate the state and unknown input of the system simultaneously. Simulation example further verifies the effectiveness of the proposed algorithm.

关键词： Iterative learning control switched system unknown input observer virtual input

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Iterative learning based fault diagnosis for discrete linear uncertain systems

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Journal of Systems engineering and Electronics 2014年第3期25卷 496-501页

作者： Wei Cao Ming Sun College of Computer and Control Engineering Qiqihar University

In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited technology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algorithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for discrete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.

关键词： discrete linear uncertain system threshold limited technology iterative learning fault estimation.

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H∞ Delayed Filtering of Markov Jump Fuzzy Systems in Consumer Electronics: Input–Output Analysis

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IEEE Transactions on Consumer Electronics 2025年

作者： Aslam, Muhammad Shamrooz Bilal, Hazrat Chang, Wen-Jer Kumar, Neeraj Khan, Izaz Ahmad Vasilakos, Athanasios V. China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221110 China College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Marine Engineering Keelung202 Taiwan Thapar University Patiala India Bacha Khan University Charsadda Department of Computer Science Pakistan Grimstad Norway

This article investigates the design of robust H∞ filters for Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, with a critical challenge in many consumer electronics applications. We extend existing research by incorporating Markovian jump parameters to model system uncertainties and considering internal–like time–varying delays, which are prevalent in real-world scenarios such as wireless communication and networked control systems in consumer devices. To address the complexities introduced by time-varying delays, we employ a three-term approximation model that more accurately captures the system dynamics compared to traditional approaches. Furthermore, we incorporate the time derivative of the membership functions (MFs) into the filter design, and its impact on system stability and performance. To ensure filter stability, we derive novel stability conditions based on the mode–dependent Lyapunov-Krasovskii functional approach, incorporating constraints on the higher bounds of the MF time derivatives. Leveraging the small–gain theorem with scaling techniques, we propose the design of both full-order and reduced-order H∞ filters, expressed in terms of linear matrix inequalities (LMIs). These LMI–based solutions provide a systematic and computationally efficient approach for filter synthesis. Finally, we validate the effectiveness of our proposed filtering strategies through illustrative examples drawn from relevant consumer electronics applications, such as noise cancellation in audio devices, image stabilization in cameras, and vibration suppression in wearable devices. The results demonstrate the enhanced robustness and performance of the proposed filters in the presence of time-varying delays and uncertainties. © 1975-2011 IEEE.

关键词： Membership functions

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Adaptive Extensive Cancellation Algorithm and Harmonic Enhanced Heart Rate Estimation based on MMWave Radar

arXiv

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arXiv 2025年

作者： Tang, Hui Yang, Zhan Rong, Yu Chai, Li Engineering Research Center of Metallurgical Automation and Measurement Technology Wuhan University of Science and Technology Wuhan430081 China The School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Heart rate (HR) monitoring is crucial for assessing physical fitness, cardiovascular health, and stress management. Millimeter-wave radar offers a promising non-contact solution for long-term monitoring. However, accurate HR estimation remains challenging in low signal-to-noise ratio (SNR) conditions. To deal with both respiration harmonics and intermodulation interference, this paper proposes a cancellation-before-estimation strategy. Firstly, we present the adaptive extensive cancellation algorithm (ECA) to suppress respiratory and its low-order harmonics. Then, we propose an adaptive harmonic enhanced trace (AHET) method to avoid intermodulation interference by refining the HR search region. Various experimental results validate the effectiveness of the proposed methods, demonstrating improvements in accuracy, robustness, and computational efficiency compared to conventional approaches based on the FMCW (Frequency Modulated Continuous Wave) system. © 2025, CC BY-NC-ND.

关键词： Intermodulation

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CAST: Contrastive Analysis of Spatial and Temporal Features for QIM-Based VoIP Steganalysis

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IEEE Signal Processing Letters 2025年 32卷 1935-1939页

作者： Zhang, Cheng Yan, Yue Jiang, Shujuan Chen, Zhong China University of Mining and Technology School of Computer Science and Technology Engineering Research Center of Mine Digitalization Ministry of Education Xuzhou 221116 China Swinburne College Shandong University of Science and Technology Shandong 250031 China China University of Mining and Technology School of Information and Control Engineering Xuzhou 221116 China

QIM(quantization index modulation)-based VoIP steganography is an information-hiding technology that malicious users could misuse to engage in illegal activities. Its countermeasure, commonly known as the QIM-based VoIP steganalysis, has been one of the research hotspots over the past decades. VoIP speech data is sequential, so most previous studies have focused on the temporal features extracted from VoIP encoding codewords to improve detection accuracy. As a result, spatial features are often ignored or fully investigated. In practice, VoIP speech data has unique spatial characteristics. Spatial features could capture the properties of nearby codewords and frames. Inspired by the success of CLIP (contrastive language–image pre-training), we propose a novel model that could efficiently incorporate spatial and temporal features named CAST (contrastive analysis of spatial and temporal features). CLIP introduces the concept of contrastive language–image learning, which has demonstrated exceptional efficiency and effectiveness in aligning textual and visual representations. However, it is not directly applicable to the field of QIM-based VoIP steganalysis. In CAST, we introduce a way to align spatial and temporal features through contrastive analysis. Experimental results demonstrate that this alignment is resource-efficient and could enhance detection accuracy. Meanwhile, CAST outperforms other state-of-the-art models in most scenarios. © 1994-2012 IEEE.

关键词： contrastive analysis Covert channel information forensics resource-efficient VoIP steganalysis

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Data security and access control method of blockchain with revocable attribute encryption in steel supply chain

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Peer-to-Peer Networking and Applications 2025年第4期18卷 1-28页

作者： Gao, Jie Zhang, Xiaohong Zhong, Shaojiang School of Information Engineering Jiangxi Province Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and Technology Ganzhou China College of Modern Economics and Management Jiangxi University of Finance and Economics Gongqingcheng China Department of Computer Science Purdue University West Lafayette USA

With the rapid development of the steel supply chain, the volume of data generated has increased dramatically, revealing potential risks associated with traditional coarse-grained data access control mechanisms and centralized architectures. To address these challenges, a blockchain-based data access control method utilizing attribute revocation ciphertext policies is proposed. This method enhances existing schemes by introducing a pre-decryption process and integrating an attribute revocation list, thereby enabling real-time attribute revocation. In terms of security, this approach is validated based on the Decisional Bilinear Diffie-Hellman (DBDH) hypothesis, which is a difficult problem under asymmetric groups, ensuring data security. By leveraging the decentralized nature of blockchain technology, this method mitigates the risks associated with single points of failure in central certification institutions. The token request mechanism introduced enables users to request multiple resources simultaneously, thereby reducing the frequency of operations. Compliant requests are authenticated, granted permissions, and subsequently returned to the user via a token. Additionally, this method integrates the Interplanetary File System (IPFS) and employs both chain-up and chain-down storage modes, effectively addressing issues related to insufficient blockchain capacity and system efficiency. The research validates the efficacy of this approach in enhancing efficiency and resolving challenges within the steel supply chain, providing substantial support for the industry’s digital transformation. Experimental results indicate that when handling large-scale attribute sets, both pre-decryption and decryption times remain stable at approximately 100 milliseconds, thereby achieving efficient and fine-grained access control to blockchain data. Overall, the scheme demonstrates excellent performance in terms of feature richness, practicality, and high efficiency, effectively mitigat

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