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

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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A Novel Set-Member Filter for Maneuver Non-Ellipsoid Extended Target Tracking With Unknown but Bounded Noises

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IEEE SENSORS JOURNAL 2025年第8期25卷 13708-13718页

作者： Ma, Tianli Sun, Hanying Wang, Kexin Chen, Chaobo Xian Technol Univ Autonomous Syst & Intelligent Control Int Joint Re Xian 710021 Peoples R China Xian Technol Univ Sch Elect Informat Engn Xian 710021 Peoples R China

For non-ellipsoid extended target tracking, the process disturbances and measurement noises are described by independent random variables, i.e., Gaussian distribution. In many real scenarios, however, the disturbances and noises are completely unknown except for bounds. This article proposes a novel set-member filter for maneuver non-ellipsoid extended target tracking with unknown but bounded (UBB) noise. The non-ellipsoid extent of the extended target is described by the random hypersurface model (RHM), the predicted state and shape parameters are obtained by calculate the Minkowski sum of the ellipsoids of the state and shape parameters and UBB noises, respectively. At the measurement update stage, the predicted shape parameters are modified based on the maneuver angle, and the minimal ellipsoid containing the pseudo-measurement nonlinear function is calculated by solving the semi-positive definite programming problem, in which the trace of the shape matrix of the pseudo-measurement nonlinear function is as the objective function. The update shape parameters are updated by calculating the intersection set of predicted shape ellipsoid and the minimal pseudo-measurement ellipsoid. An evaluation of performance in a simulated environment shows that the proposed SMF-NEET approach outperforms the existing adaptive extended Kalman filter based on a RHM (RHM-AEKF) and unscented Kalman filter based on the random hypersurface model (RHM-UKF) methods with up to 83.9% and 88.9% improvement in the accuracy in position, 40.3% and 58.2% in velocity, 26.6% and 31.5% in the shape, respectively, while tracking a maneuver non-ellipsoid extend target with UBB Noises.

关键词： Shape Noise Ellipsoids Target tracking Kinematics Noise measurement Shape measurement Kalman filters filtering algorithms Antenna measurements Extended target non-ellipsoid extension random hypersurface model (RHM) semi-positive definite programming set-member filter unknown but bounded (UBB) noises

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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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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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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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Rethinking the Designing of Convolution Engine for Reconfigurable CNN Accelerator Using Sparse-Based Design Scheme

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Meng, Yishuo Wang, Jianfei Xiang, Siwei Hou, Jia Lin, Zhijie Mei, Kuizhi Yang, Chen Xi An Jiao Tong Univ Sch Microelect Xian 710049 Shaanxi Peoples R China

Convolutional neural networks (CNNs) are evolving as they are applied to more diverse environments and more difficult challenges. The evolving induces various convolution modes (e.g., 1x1 convolution, 2-stride convolution and rectangle convolution) in current CNNs and makes it difficult for the hardware accelerators to efficiently support such various convolution modes. In this paper, it is found that an important difference of these convolution modes is the computation density. Therefore, the above convolution modes are regarded as structured sparse and claims that sparse-based design methodology can be applied for the implementation of the reconfigurable CNN accelerator. Subsequently, two critical architectural parameters, including input tile size and convolution engine (CE) scale, are evaluated based on Standard deviation of calculations (SDC), unsupported convolution mode (UCM) and unsuitable IFM size (UIS), DSP utilization ratio (DUR) as well as hardware resource overhead (HRO), respectively. With the aid of the optimal parameters, a high-parallelism and flexible CE array and a high-performance and reconfigurable CNN architecture are designed. The accelerator was implemented on a Xilinx VC709 FPGA and ran at a clock frequency of 300 MHz, achieving 921.60 to 1382.40 GOPS while supporting various convolution modes. Compared with previous dense-/sparse-based works, the proposed accelerator can realize 1.35x to 10.77x improvements on performance and 1.22x to 2.84x improvements on DSP efficiency while deploying VGG16.

关键词： Convolution Convolutional neural networks Space exploration Hardware acceleration filtering algorithms Design methodology Reconfigurable architectures Information filters Engines Standards Convolutional neural network reconfigurable accelerator design space exploration DSP efficiency

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Multi-shelf graphic equalizer

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SIGNAL PROCESSING 2025年 226卷

作者： Schlecht, Sebastian J. Sinjanakhom, Tantep Vaelimaekia, Vesa Aalto Univ Dept Informat & Commun Engn Acoust Lab Espoo 02150 Finland Friedrich Alexander Univ Erlangen Nurnberg Multimedia Commun & Signal Proc D-91054 Erlangen Germany

A graphic equalizer (GEQ) is a standard tool in audio production and effect design. Adjustable gain control frequencies are fixed along the logarithmic frequency axis, and an automatic design method matches the magnitude response to them whenever target gains are changed. Most commonly, the GEQ comprises a set of peak filters centered an octave apart, possibly with a shelving filter at the bottom and top of the frequency range. While accurate designs were proposed, the dynamic range is typically limited to 24 dB. In this paper, we propose two innovations. First, we introduce a GEQ based on shelving filters only, which can cover an extensive dynamic range of over 60 dB. Secondly, we introduce an order-switching technique that combines shelf filters of different order. We demonstrate the performance and advantages of the proposed filter with design examples. The proposed shelf-filter-based GEQ offers a wider dynamic range and a smoother magnitude response than traditional peak-filter-based GEQ designs.

关键词： Audio systems Digital filters filtering algorithms IIR filters Optimization

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Optimal Speech Signal Processing Based on Dynamic Multi-Scale Analysis

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IEEE ACCESS 2025年 13卷 22946-22955页

作者： Guo, Zefei Li, Xiao Zhang, Yuchen Tibet Univ Coll Informat Sci & Technol Lhasa 850000 Peoples R China

To enhance the quality of speech signals, this paper introduces a novel speech signal processing method that integrates Dynamic Multi-Scale (DMS) and Adaptive Error Minimization (AEM) techniques. This method significantly enhances noise reduction and signal fidelity in dynamic environments, distinguishing itself from previous approaches through its real-time adaptive filtering, which makes it highly adaptable to complex, non-stationary noise conditions. The proposed method is grounded in dynamic multi-scale analysis, employing multi-scale decomposition of speech signals to optimize their time-frequency characteristics and dynamic adjustments, thereby forming a new noise reduction approach, DMS. Initially, the multi-scale decomposition technique effectively captures the multi-scale features of noisy speech signals. Subsequently, optimizing the time-frequency characteristics and dynamic signal adjustments effectively removes noise while improving the signal's time-frequency resolution. Finally, the method is further enhanced through the adaptive error minimization algorithm, leading to a more pronounced noise reduction effect. Experimental results demonstrate that the proposed method outperforms the single dynamic multi-scale technique in terms of improving signal-to-noise ratio (SNR).

关键词： Noise Heuristic algorithms Signal processing algorithms Noise reduction Minimization Speech enhancement Time-frequency analysis Optimization Signal processing filtering algorithms Adaptive error multi-scale analysis optimal estimation speech noise reduction time-frequency characteristic optimization

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