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Command-Filter-Based Fixed-Time Prescribed Tracking Switching Control for Nonlinear Systems With Unknown Control Coefficients

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第6期PP卷 PP页

作者： Hua, Changchun Pan, Wenlong Li, Hao Li, Qidong Yanshan Univ Inst Elect Engn Qinhuangdao 066004 Peoples R China Hebei Univ Sci & Technol Inst Elect Engn Shijiazhuang 050018 Peoples R China

This article investigates fixed-time prescribed tracking control based on a command filter for a class of nonlinear systems with unknown control coefficients. A novel switching control mechanism is proposed to address the challenge of unknown control coefficients and introduce a dual-parameter switching strategy with online parameter adjustment based on the designed conditions. To address the limitation in existing research, where prescribed performance functions depend on the initial conditions of systems, this work designs a new class of prescribed performance functions that eliminates this dependency. A command-filter-based backstepping approach effectively avoids the computational complexity of high-order derivatives in traditional backstepping methods. In addition, the issue of the nondifferentiability of the virtual controller at switching moments in existing switching control methods has been resolved. Ultimately, the boundedness of all signals in the closed-loop system is ensured. Moreover, a simulation example of a second-order system verifies the effectiveness of the algorithm in this article.

关键词： Control systems Switches Nonlinear systems filtering theory Backstepping filtering algorithms filtering Control theory Target tracking System performance Command filter backstepping fixed-time prescribed tracking switching control unknown control coefficients

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Power-Enhanced GNSS Interference Mitigation With Sensed Equivalent Bandwidth Based on ASF Algorithm

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IEEE SENSORS JOURNAL 2025年第2期25卷 2886-2896页

作者： Song, Jie Lu, Zukun Xiao, Wei Li, Chenglong Wang, Yuqing Sun, Guangfu Natl Univ Def Technol Coll Elect Sci & Technol Changsha 410073 Peoples R China Key Lab Satellite Nav Technol Changsha 410073 Peoples R China Hunan Inst Adv Technol Yueyang 414015 Peoples R China

Power-enhancement is a primary focus in the global navigation satellite system (GNSS) to improve its anti-interference capabilities. However, interference issues cannot be fully resolved due to the limited increase in signal power. Additionally, traditional interference mitigation methods often misclassify signals as interference and suppress them, leading to significant GNSS signal loss. This article proposes an adaptive sparse filtering (ASF) method based on sensed equivalent bandwidth, enabling interference mitigation for GNSS power-enhancement without additional signal loss. By analyzing the spectral relationship between interference and signal bandwidth, the equivalent bandwidth that ignores the strong signal recognition is derived, and the sparse sampling model is developed to reconstruct the interference mitigation filter. Simulations and practical tests verify the effectiveness of this interference mitigation method, which is broadly applicable to both conventional and power-enhanced GNSS signals. It improves anti-interference performance by 3-5 dB without introducing additional computational complexity associated with spectral awareness. This improvement in signal quality also results in the 19% increase in positioning accuracy.

关键词： Interference Global navigation satellite system filtering algorithms Prevention and mitigation Signal processing algorithms Receivers filtering theory Satellites Information filters Vectors Adaptive sparse filtering (ASF) global navigation satellite system (GNSS) power-enhancement high anti-interference performance interference mitigation low-cost sensed equivalent bandwidth

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A Uniform Framework of Yau-Yau Algorithm Based on Deep Learning With the Capability of Overcoming the Curse of Dimensionality

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第1期70卷 339-354页

作者： Chen, Xiuqiong Sun, Zeju Tao, Yangtianze Yau, Stephen S. -T. Renmin Univ China Sch Math Beijing 100872 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China Beijing Inst Math Sci & Applicat Beijing 101408 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China

In numerous application areas, high-dimensional nonlinear filtering is still a challenging problem. The introduction of deep learning and neural networks has improved the efficiency of classical algorithms and they perform well in many practical tasks. However, a theoretical interpretation of their feasibility is still lacking. In this article, we exploit the representational ability of recurrent neural networks (RNNs) and provide a computationally efficient and optimal framework for nonlinear filter design based on the Yau-Yau algorithm and RNNs. Theoretically, it can be proved that the size of the neural network required in this algorithm increases only polynomially rather than exponentially with dimension, which implies that the Yau-Yau algorithm based on RNNs has the ability to overcome the curse of dimensionality. Numerical results also show that our method is more competitive than classical algorithms for high-dimensional problems.

关键词： Filters Recurrent neural networks Probability density function Kalman filters filtering algorithms Covariance matrices Stochastic processes Curse of dimensionality nonlinear filtering recurrent neural networks (RNNs) Yau-Yau algorithm

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High-Quality Reconstruction of Depth Maps From Graph-Based Non-Uniform Sampling

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 780-791页

作者： Yang, Jingyu Xu, Wenqiang Hou, Yusen Ye, Xinchen Frossard, Pascal Li, Kun Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Dalian Univ Technol DUT RU Int Sch Informat Sci & Engn Dalian 116024 Peoples R China Ecole Polytech Fed Lausanne Signal Proc Lab CH-999034 Lausanne Switzerland Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

Depth sensing is essential for intelligent computer vision applications, but it often suffers from low range precision and spatial resolution. To address this problem, we propose a novel framework that combines non-uniform sampling and reconstruction based on graph theory. Our framework consists of two main components: (1) a graph Laplacian induced non-uniform sampling (GLINUS) scheme that samples depth signals more densely around edges and contours than in smooth regions, and (2) an ensemble of priors (EoP) model that reconstructs the high-quality depth map using adaptive dual-tree discrete wavelet packets (ADDWP) transform, graph total variation regularizer, and graph Laplacian regularizer with color guidance. We solve the reconstruction problem using the alternating direction method of multipliers (ADMM). Our experiments demonstrate that our framework can capture fine structures and global information in depth signals and produce superior depth reconstruction results.

关键词： Image reconstruction Laplace equations Spectral analysis Color Task analysis filtering algorithms Image color analysis Graph signal processing non-uniform sampling depth reconstruction

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Multi-Population Kidney-Inspired Algorithm With Migration Policy Selections for Feature Selection Problems

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IEEE ACCESS 2025年 13卷 6306-6320页

作者： Jaddi, Najmeh Sadat Abdullah, Salwani Leng Goh, Say Zakree Ahmad Nazri, Mohd Othman, Zalinda Kamrul Hasan, Mohammad Alvankarian, Fatemeh Iranian eUnivers Fac Comp Engn Tehran *** Iran Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia Univ Malaysia Sabah Kampus Antarabangsa Labuan Fac Comp & Informat Optimizat & Visual Analyt Res Grp Labuan 87000 Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Cyber Secur Bangi 43600 Selangor Malaysia Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA

Optimization algorithms often encounter challenges in effectively managing the trade-off between exploration and exploitation, usually leading to less-than-optimal outcomes. This study introduces two novel migration policies in multi-population version of kidney-inspired algorithm (KA) to address this dilemma. The initial algorithm, coded as MultiPop-KA, implements a predetermined migration policy. Conversely, the second algorithm, coded as AutoMultiPop-KA, adopts an adaptive migration policy selection process that determines migration type based on the average fitness of sub-populations. By capitalizing on a multi-population framework and incorporating two migration policies, these methods aim to achieve a more refined equilibrium between exploration and exploitation, thereby augmenting the effectiveness of the KA. Experimental evaluations, conducted across 25 test functions and applied to 18 benchmark feature selection problems, demonstrate the efficacy of the proposed techniques. These results indicate that the proposed approach can significantly enhance optimization algorithms' performance and overall quality.

关键词： Optimization Feature extraction Rough sets Filtration Convergence Blood Kidney Linear programming Information science filtering algorithms Exploration and exploitation kidney-inspired algorithm multi-population migration policy feature selection

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Design Method for Polynomial Orders in ARX-Based Model-Free Predictive Controllers

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2025年第1期13卷 604-614页

作者： Cartes, Bryan Burgos, Patricio Cifuentes, Claudio A. Young, Hector Wei, Yao Rojas, Christian A. Rodriguez, Jose Univ La Frontera Dept Elect Engn Temuco 4811230 Chile Chinese Acad Sci Quanzhou Inst Equipment Mfg Haixi Inst Quanzhou 362200 Peoples R China Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso 234000 Chile Univ San Sebastian Fac Engn Santiago 8370146 Chile

Model-free (MF) strategies have emerged as a promising solution to challenges associated with modeling errors and uncertainties in predictive control (PC) systems. In this context, MF-PC schemes utilizing Auto-Regressive with eXogenous input (ARX) time-series models offer a flexible approach for online predictor construction using input-output data. However, the design of the polynomial orders within the ARX structure is critical, as it determines the balance between model accuracy and computational cost. This article presents a novel systematic method for designing ARX polynomial orders in MF-PC, based on well-established statistical criteria. Unlike traditional trial-and-error approaches, the proposed method offers simplicity and efficiency, allowing for accurate designs using general information about the controlled system. To demonstrate its feasibility, the proposed method is applied to the design of a MF-PC voltage control of a grid-forming inverter (GFI). Experimental trials conducted in a laboratory-scale GFI validate the effectiveness of the proposed method, delivering robust and accurate reference tracking under set-point and load disturbances. A comparison with conventional model-based PC highlights the advantages of MF-PC with an ARX predictor designed using the proposed methodology in the presence of model uncertainty.

关键词： Predictive models Mathematical models Polynomials Prediction algorithms Computational modeling Voltage control Load modeling Accuracy filtering algorithms Data models LCL filter model parameter mismatch parameter estimation predictive control (PC)

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HAST: A Hardware-Efficient Spatio-Temporal Correlation Near-Sensor Noise Filter for Dynamic Vision Sensors

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第3期72卷 1332-1345页

作者： Gopalakrishnan, Pradeep Kumar Chang, Chip-Hong Basu, Arindam CNRS CREATE Ltd Singapore 138602 Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

The Dynamic Vision Sensor (DVS) is a bio-inspired image sensor which has many advantages such as high dynamic range, high bandwidth, high temporal resolution and low power consumption for Internet of Video Things and Edge Computing applications. However, spuriously generated Background Activity (BA) noise events can significantly degrade the quality of DVS output and cause unnecessary computations throughout the image processing chain, reducing its energy efficiency. Near-sensor filters can mitigate this problem by preventing the BA noise events from reaching downstream stages. In this paper, we propose a novel, hardware-efficient, spatio-temporal correlation filter (HAST) for near-sensor BA noise filtering. It uses compact two-dimensional binary arrays along with simple, arithmetic-free hash-based functions for storage and retrieval operations. This approach eliminates the need to use timestamps for determining the chronological order of events. HAST uses much lower memory and energy compared to other hardware-friendly filters (BAF/STCF) while matching their performance in simulations with standard datasets;for a sensor of resolution 346 x 260 pixels, it requires only 5-18% of their memory, and about 15% of their energy per event for correlation time tau ranging from 1 to 50 ms. The memory and energy gains of the filter increase with sensor resolution. In FPGA implementation, HAST achieves about 29% higher throughput than BAF/STCF while utilizing only about 5% of their memory. The filter parameter values can be chosen by Design Space Exploration (DSE) for optimized performance-resource trade-offs based on application requirements.

关键词： Noise Voltage control Memory management filtering algorithms Correlation Information filters Vision sensors Robot sensing systems Energy resolution Field programmable gate arrays Dynamic vision sensor neuromorphic vision sensor background activity noise spatio-temporal correlation filtering hash function field programmable gate arrays

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Achieving Faster and Smarter Chest X-Ray Classification With Optimized CNNs

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IEEE ACCESS 2025年 13卷 10070-10082页

作者： Louati, Hassen Louati, Ali Mansour, Khalid Kariri, Elham Kingdom Univ Coll Informat Technol Riffa 40434 Bahrain Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Informat Syst Al Kharj 11942 Saudi Arabia Arab Open Univ Fac Comp Studies Aali 3220 Bahrain

X-ray imaging is essential in medical diagnostics, particularly for identifying anomalies like respiratory diseases. However, building accurate and efficient deep learning models for X-ray image classification remains challenging, requiring both optimized architectures and low computational complexity. In this paper, we present a three-stage framework to enhance X-ray image classification using Neural Architecture Search (NAS), Transfer Learning, and Model Compression via filter pruning, specifically targeting the ChestX-Ray14 dataset. First, NAS is employed to automatically discover the optimal convolutional neural network (CNN) architecture tailored to the ChestX-Ray14 dataset, reducing the need for extensive manual tuning. Subsequently, we leverage transfer learning by incorporating pre-trained models, which enhances the model's generalizability and reduces dependency on large volumes of labeled X-ray data. Finally, model compression through filter pruning, driven by evolutionary algorithms, trims redundant parameters to improve computational efficiency while preserving model accuracy. Experimental results demonstrate that this approach not only boosts classification accuracy on the ChestX-Ray14 dataset but also significantly reduces model size, making it suitable for deployment in resource-constrained environments, such as mobile and edge devices. This framework provides a practical, scalable solution to improve both the accuracy and efficiency of medical image classification.

关键词： Computational modeling Accuracy Convolutional neural networks X-ray imaging Transfer learning Computer architecture Computational efficiency Solid modeling Optimization filtering algorithms CNN optimization NAS transfer learning model compression medical diagnostics

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Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection

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IEEE ACCESS 2025年 13卷 618-632页

作者： Guo, Shuangle Li, Yongxia Zhang, Jianguang Liu, Yue Tian, Tian Guo, Mengchen Shandong Univ Aeronaut Sch Informat Engn Binzhou 256600 Shandong Peoples R China Hengshui Univ Off Acad Affairs Hengshui 053000 Peoples R China Hengshui Univ Dept Math & Comp Sci Hengshui 053000 Peoples R China Hengshui Univ Off Human Resources Hengshui 053000 Peoples R China Univ Calif Davis Dept Appl Math Davis CA 95616 USA Xiamen Univ Malaysia Dept Elect & Elect Engn Sepang 43900 Malaysia

Feature selection is an important preprocessing step in machine learning to remove irrelevant and redundant features. Due to its ability to effectively maintain the discriminability of extracted features, Trace Ratio Linear Discriminant Analysis (TR-LDA) has become the foundation for many feature selection algorithms. As is known, TR-LDA is a challenging problem to solve because of its trace-ratio form, and it also faces the scale invariance problem. These two drawbacks of TR-LDA significantly reduce the performance of feature selection algorithms based on it. To overcome these drawbacks, this paper proposes the sparse LDA with constant between-class distance (SLDA-CBD) model to select relavant features. This model first transforms TR-LDA into a non-trace ratio problem with a constant between-class distance constraint, and then imposes row constraints on the projection matrix to implement feature selection. Since the SLDA-CBD model is rooted in TR-LDA, it ensures the discriminative performance of the selected features. The constant between-class distance constraint successfully avoids the scale invariance problem. Additionally, due to the non-trace ratio form of the SLDA-CBD model, it is easily solvable. The experimental results show that the proposed method has better performance compared to the baseline and six state-of-the-art relative methods, with improvements of over 1% on image datasets and over 2% on video datasets in most cases, while also demonstrating high stability, proving its effectiveness and advantage in practical applications.

关键词： Feature extraction Classification algorithms Training filtering algorithms Computational modeling Machine learning algorithms Sparse matrices Mathematical models Vectors Predictive models Feature selection TR-LDA sparse regression

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An Evolutionary Toolchain for Morphological Filter Mapping on Many-Core Architectures

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IEEE ACCESS 2025年 13卷 54350-54366页

作者： Pedrino, Emerson C. Lima, Denis P. Gallon, Igor F. Roda, Valentin O. Liu, Naijia Tempesti, Gianluca Univ Fed Sao Carlos Dept Comp Sci BR-13565905 Sao Carlos Brazil Univ Fed Rio Grande do Norte Elect Engn Dept BR-59078900 Natal Brazil Univ York Sch Phys Engn & Technol York YO10 5DD England

Many-core systems are systolic architectures consisting of an arbitrarily large number of processing nodes connected by a point-to-point communication network. Their architecture makes them ideally suited for the implementation of data-flow algorithms, of which Mathematical Morphology (MM) filters are a typical example. However, the performance of data-flow applications on many-core systems is highly dependent on the quality of the mapping of the application tasks to the computational cores. Decomposing the structuring elements of morphological operations improves their performance, however, performing such decomposition on many-core systems leads to increased communication. The need to find a balance between performance and communication is a representative example of the general problem of mapping optimizations. The approach presented in this paper explores a two-phase design-time optimization toolchain based on evolutionary algorithms: a front-end single-objective algorithm decomposes a MM filter using smaller operators, while a back-end multi-objective (fault-tolerance, energy, and communication) algorithm searches for optimal mappings of the filter on a specific many-core system, taking into account the architectural parameters of the hardware. The output of the toolchain is a Pareto front of mapping solutions, allowing the designer to select an implementation that matches application-specific requirements. A set of standard benchmark applications was used to determine the optimal parameters for the algorithms, which were then validated on two real-world application examples involving the detection of features in high-resolution PCB images. Two application mapping experiments focusing on energy constraints were conducted, in which the proposed procedure was compared to deterministic mapping techniques. The evolutionary procedure was observed to offer a significant advantage over the deterministic approach, with percentage gains of up to 73.78% for smaller grids

关键词： Computer architecture Erosion Electronic mail Parallel processing Morphology filtering algorithms Hardware Translation Memory management Focusing Image processing many-core systems mathematical morphology multiobjective optimization tasks mapping

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