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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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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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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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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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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55卷 2844-2851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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MusicReco: Interactive Interface Modeling With User-Centered Design in a Music Recommendation System

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IEEE ACCESS 2025年 13卷 30058-30087页

作者： Frantzvaag, Mats Ottem Chatterjee, Ayan Ghose, Debasish Dash, Soumya P. Kristiania Univ Coll Sch Econ Innovat & Technol N-5022 Bergen Norway STIFTELSEN NILU Dept Digital Technol N-2007 Kjeller Norway Simula Metropolitan Ctr Digital Engn Dept Holist Syst N-0170 Oslo Norway Indian Inst Technol Bhubaneswar Dept Informat & Commun Technol Bhubaneswar 752050 India

Recommendation technologies are widespread in streaming services, e-commerce, social media, news, and content management. Besides recommendation generation, its presentation is also important. Most research and development focus on the technical aspects of recommendation generation;therefore, a gap exists between recommendation generation and its effective presentation and user interaction. This study focuses on how personalized recommendations can be presented and interacted with in a music recommendation system using interactive visual interfaces. Interactive interface modeling with User-Centered Design (UCD) in a recommendation system is essential for creating a user-friendly, engaging, and personalized experience. By involving users in the recommendation process and considering their feedback, the system can deliver more relevant content, foster user trust, and improve overall user satisfaction and engagement. In this study, the visual interface design and development of a personalized music recommendation prototype (MusicReco) are presented using an iterative UCD approach, involving twenty end-users, one researcher, three academic professionals, and four experts. As the study is more inclined toward the recommendation presentation and visual modeling, we used a standard content-based filtering algorithm on the publicly available Spotify dataset for music recommendation generation. End-users helped to mature the MusicReco prototype to a basic working version through continuous feedback and design inputs on their needs, context, preferences, personalization, and effective visualization. Moreover, MusicReco captures the idea of mood-based tailored recommendations to encourage end-users. Overall, this study demonstrates how UCD can enhance the presentation and interaction of mood-based music recommendations, effectively engaging users with advancements in recommendation algorithms as a future focus.

关键词： Recommender systems Music User centered design Prototypes Iterative methods Conferences Software engineering Internet filtering algorithms Music recommendation system visualization user-centered design interface design iterative approach content-based filtering content-based filtering

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A Novel Mixture Least Squares Approach for Simultaneous Parameter/State and Unknown Input Estimation

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2025年第2期70卷 1252-1258页

作者： Ding, Bo Wei, Yuanchu Zhang, Yong Yang, Wu Yangzhou Univ Coll Informat Engn Yangzhou 225127 Peoples R China Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

In this article, we introduce a novel mixture least squares (MLSs) algorithm to deal with the problems of simultaneous parameter/state and unknown input estimation. First, the MLSs algorithm is derived to estimate the desired parameter and unknown input, which can be regarded as a unified framework for deterministic least squares and stochastic least squares. The unbiasedness and optimality of the MLSs estimators are further verified. Then, based on the established MLSs algorithm, a new solution to simultaneous state and unknown input estimation (SUIE) problems is given. The proposed method is more concise and straightforward than the existing SUIE algorithms. The method provided in this article offers fresh insight into parameter/state estimation with unknown input.

关键词： Covariance matrices Estimation Stochastic processes Maximum likelihood estimation Kalman filters Vectors filtering algorithms Linear stochastic systems mixture least squares (MLSs) parameter/state estimation unknown input estimation

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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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Combating Noisy Labels by Alleviating the Memorization of DNNs to Noisy Labels

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IEEE TRANSACTIONS ON MULTIMEDIA 2025年 27卷 597-609页

作者： Yuan, Shunjie Li, Xinghua Miao, Yinbin Zhang, Haiyan Liu, Ximeng Deng, Robert H. Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China Singapore Management Univ Sch Informat Syst Singapore 178902 Singapore

Data is the essential fuel for deep neural networks (DNNs), and its quality affects the practical performance of DNNs. In real-world training scenarios, the successful generalization performance of DNNs is severely challenged by noisy samples with incorrect labels. To combat noisy samples in image classification, numerous methods based on sample selection and semi-supervised learning (SSL) have been developed, where sample selection is used to provide the supervision signal for SSL, achieving great success in resisting noisy samples. Due to the necessary warm-up training on noisy datasets and the basic sample selection mechanism, DNNs are still confronted with the challenge of memorizing noisy samples. However, existing methods do not address the memorization of noisy samples by DNNs explicitly, which hinders the generalization performance of DNNs. To alleviate this issue, we present a new approach to combat noisy samples. First, we propose a memorized noise detection method to detect noisy samples that DNNs have already memorized during the training process. Next, we design a noise-excluded sample selection method and a noise-alleviated MixMatch to alleviate the memorization of DNNs to noisy samples. Finally, we integrate our approach with the established method DivideMix, proposing Modified-DivideMix. The experimental results on CIFAR-10, CIFAR-100, and Clothing1M demonstrate the effectiveness of our approach.

关键词： Noise measurement Training Artificial neural networks Noise Image classification Entropy filtering algorithms Accuracy Semisupervised learning Reviews Deep neural networks image classification. label flipping attack noisy label learning sample selection

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